Topics: AI | Data Science | Data Visualization | Deep Learning | Machine Learning | NLP | Python | R | Statistics

Search results for Financial Risk Management

    Found 256 documents, 12483 searched:

  • AWS Data Exchange Webinar: Maintain competitive edge with third-party financial services data

    Join this webinar, Nov 11, to learn how leveraging third-party financial services data can facilitate faster, intelligence-based decision-making that propels your company's business outcomes and digital transformation.

  • Capitalize on Your Analytics Skills With the Johns Hopkins SAIS MA in Global Risk (Online)

    The Johns Hopkins SAIS MA in Global Risk (online) program prepares graduates to gain experience analyzing and addressing real-world scenarios, and the knowledge to fine-tune your analyses with insights from economics, history and political science. Enroll now and join a highly active, international community of more than 230,000 Johns Hopkins alumni.

  • Deep Learning in Finance: Is This The Future of the Financial Industry?

    Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.

  • Four Ways to Apply NLP in Financial Services

    Natural language processing (NLP) is increasingly used to review unstructured content or spot trends in markets. How is Refinitiv Labs applying NLP in financial services to meet challenges around investment decision-making and risk management?

  • Optimization with Python: How to make the most amount of money with the least amount of risk?

    Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobel-prize winning economic theory for maximizing investment profits while minimizing risk.

  • Can’t-Miss Keynote Speakers at PAW Financial, plus 3 other PAWs in Vegas – Save ’til March 8

    Predictive Analytics World for Financial is heading to Las Vegas, NV on Jun 16-20, and we're excited to announce the speaker line-up. The year’s only PAW Financial will be held alongside PAW Business, PAW Healthcare, PAW Industry 4.0, and Deep Learning World. Register now!

  • Can’t-Miss Keynotes at PAW Financial, plus 3 other PAWs in Vegas – Save ’til April 27

    Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming Predictive Analytics World for Financial conference in Las Vegas, Jun 3-7.

  • New Book: Credit risk analytics, The R Companion

    Credit risk analytics in R will enable you to build credit risk models from start to finish, with access to real credit data on accompanying website, you will master a wide range of applications.

  • Webinar: Minimizing Model Risk with Automated Machine Learning, Jan 31

    See how banks can use Automated Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements.

  • Three Predictive Analytics Events in NYC – Business, Financial, Healthcare, Oct 29 – Nov 2

    Predictive Analytics World, the leading cross-vendor event for data science and machine learning professionals will be at NYC, Oct 29-Nov 2. Pre-conference rates end Oct 27.

  • Willis Towers Watson: Data Scientist – Financial Services

    Seeking a Data Scientist with a passion for delivering innovative analytical solutions. You will deliver high quality work for our broad set of UK clients, working on projects including behavioural modelling, price optimisation, financial risk modelling and big data analytics.

  • Discover Financial Services: Manager, Decision Analytics

    Seeking an analytics professional with work experience in Banking, Financial or Insurance area in developing and evaluating business rules and strategies using advanced statistical techniques.

  • Data Analytics Models in Quantitative Finance and Risk Management

    We review how key data science algorithms, such as regression, feature selection, and Monte Carlo, are used in financial instrument pricing and risk management.

  • Introducing Predictive Analytics World Financial, Oct 24-27, New York City

    PAW Financial focuses on analytics needs of banks, insurance companies, credit card companies, investment firms, and other financial institutions. Book now for the early bird rates, and save extra with code KDN150.

  • Predictive Analytics World in October: Government, Business, Financial, Healthcare

    Plan ahead and save on Predictive Analytics World conferences this October. Early bird savings are still available, and save extra with with KDnuggets code KDN150.

  • Axcess-Financial: Manager of Governance – Financial Services Industry

    Seeking a Manager of Governance, to develop an Operation Risk Management (ORM) Framework and install best practices in 2nd line ORM oversight across the company, with goal to ensure compliance with risk policies and standards.

  • Hiring? Approving Mortgages? It’s the Same Thing (Risk …)

    Traditionally hiring and approving mortgage are completely different problems. But, when you look at them from a data science perspective, both things do have similar characteristics.

  • Renovate America: Sr. Data Scientist – Origination Risk

    Quantitative analysis with experience working in the financial industry. Work together with the Risk Management team and focus on origination risk for unsecured lending products.

  • New York Community Bank: Sr. Stress Testing Quantitative Risk Modeler

    Provide risk management and measurement services in the areas of credit risk, market risk, liquidity risk, operational risk and model risk.

  • Stanford: Online Certificates in Data Mining and Management

    Learn the science and art of managing people, products, processes, and data with from Stanford world-famous faculty with online courses like "Mining Massive Data Sets" or "Intro to Statistical Learning".

  • Equifax: Model Risk Management Model Specialist

    Perform and lead activities related to model risk matters surrounding statistical model development and validation at Equifax.

  • Paychex: Risk Modeling Analyst II

    Perform high level data mining and analytical projects, develop predictive models, understands specific business details, and interact with data owners/key stakeholders.

  • Interview: Tom Kern, Risk Modeling Manager, Paychex on Risk Analytics and Sales Anticipation Model

    We discuss the role of Risk Analytics at Paychex, strategic importance of Sales Anticipation Model, optimizing business processes by leveraging Big Data, and advice for companies thinking about Big Data as well as aspiring students.

  • Risk Analysis and Credit Scoring

    Algolytics, offers analytical solutions for financial institutions, including Credit Scoring, Fraud Detection, and Survival Time Analysis. ArrowModel, an integrated scoring environment, which combines powerful statistical Read more »

  • Risk Analyst

    Providing analytical support on risk management initiatives for policies to control risks associated on-line money movement, and consulting assistance to client financial institutions in establishing and monitoring their own risk criteria.

  • Risk Modeling Analyst

    Develop and implement scorecards/data driven predictive models covering different areas of the company, including enterprise risk management, marketing, sales and field operations.

  • Credit Risk Manager

    Provide overall leadership in innovating, building, and implementing risk based pricing solutions that increase the value of underwriting for lending operations.

  • Dr. Data Show Video: What the Hell Does “Data Science” Really Mean?

    The latest episode of the Dr. Data Show answers the question, "What the hell is data science?"

  • A Guide to 14 Different Data Science Jobs">Silver BlogA Guide to 14 Different Data Science Jobs

    The field of data science is growing into one that features a variety of job titles This guide reviews different positions available for you to consider if you have a data science background.

  • Scale and Govern AI Initiatives with ModelOps

    AI/ML model life cycle automation and orchestration ensures reliable model operations and governance at scale. The path to production for each enterprise model can vary, along with different monitoring, continuous improvement, retirement needs. Organizations must now consider ModelOps as a fundamental capability towards operational excellence and immediate ROIs.

  • 9 Outstanding Reasons to Learn Python for Finance

    Is Python good for learning finance and working in the financial world? The answer is not only a resounding YES, but yes for nine very good reasons. This article gets into the details behind why Python is a must-know programming language for anyone who wants to work in the financial sector.

  • Django’s 9 Most Common Applications">Gold BlogDjango’s 9 Most Common Applications

    Django is a Python web application framework enjoying widespread adoption in the data science community. But what else can you use Django for? Read this article for 9 use cases where you can put Django to work.

  • MLOps Best Practices

    Many technical challenges must be overcome to achieve successful delivery of machine learning solutions at scale. This article shares best practices we encountered while architecting and applying a model deployment platform within a large organization, including required functionality, the recommendation for a scalable deployment pattern, and techniques for testing and performance tuning models to maximize platform throughput.

  • AWS Webinar: How are data-driven companies using ESG and sustainability data to make actionable decisions?

    In this virtual session, on Jul 29 @ 11AM PT, 2PM ET, our panel of experts will uncover how companies across several verticals use ESG data to move beyond the reporting benchmark, deepen business insights, and create competitive differentiation.

  • Using External Data to Accelerate Business in a Post-Vaccinated World

    Join this webinar, Jun 24, 2021, to learn how companies are developing insights to better prepare for growth opportunities, improve business performance and mitigate risk in a post-pandemic economy.

  • Awesome list of datasets in 100+ categories

    With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.

  • How to Apply Transformers to Any Length of Text

    Read on to find how to restore the power of NLP for long sequences.

  • Must Know for Data Scientists and Data Analysts: Causal Design Patterns">Silver BlogMust Know for Data Scientists and Data Analysts: Causal Design Patterns

    Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for measuring causal effects from observational data.

  • Data Science and Analytics Career Trends for 2021

    Let's check out what are the new data science and analytics career trends for 2021 that may also shape the career options in the future.

  • Applications of Data Science and Business Analytics

    In recent times, a large number of businesses have begun realising the potential of Data Science. Business analytics and data science applications are far and wide. So let us have a look at them in detail.

  • Before Probability Distributions

    Why do we use probability distributions, and why do they matter?

  • A Layman’s Guide to Data Science. Part 3: Data Science Workflow">Gold BlogA Layman’s Guide to Data Science. Part 3: Data Science Workflow

    Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.

  • Free Economics & Finance Courses for Data Scientists

    Here is a selection of courses for those interested in diversifying their domain knowledge into the related realms of economics and finance, with the goal of being able to apply your data science skills to these domains.

  • Top Process Mining Software Companies, Updated

    Understanding the real business processes of a company through analysis of its information systems can guide digital transformations. Here, the top 10 process mining software companies are reviewed that can assist businesses in process optimizations through unique insights of business systems.

  • 50 Must-Read Free Books For Every Data Scientist in 2020">Silver Blog50 Must-Read Free Books For Every Data Scientist in 2020

    In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science.

  • How Bad Data is Affecting Your Organization’s Operational Efficiency

    Despite recognizing the importance of data quality, many companies still fail to implement a data quality framework that could protect them from making costly mistakes. Poor data does not just cause revenue loss – it’s the reason your company could lose employees, customers and reputation!

  • Fidelity on How to Find a Tailor-Fit Unicorn Data Scientist

    Predictive Analytics World for Financial Services in Las Vegas, May 31-Jun 4 is honored to host an exceptional keynote by Fidelity Investments’ AI and Data Science Center of Excellence Leader, Victor Lo: "How to Find a Tailor-Fit 'Unicorn' Data Scientist for Financial Services". Use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.

  • The Ultimate Guide to Model Retraining

    Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. So, retraining and redeploying will likely be required. In other words, deployment should be treated as a continuous process. This guide defines model drift and how to identify it, and includes approaches to enable model training.

  • AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020">Silver BlogAI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020

    We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.

  • Artificial Friend or Virtual Foe

    Is AI making more good than harm?

  • Top 7 Data Science Use Cases in Trust and Security

    What are trust and safety? What is the role of trust and security in the modern world? Read this overview of 7 data science application use cases in the realm of trust and security.

  • Would you buy insights from this guy? (How to assess and manage a Data Science vendor)

    With all the hype from data science vendors selling "actionable insights" to boost your company's bottom line, selecting your analytics partner should proceed through the same, careful process as any traditional business endeavor. Follow these questions and best practices to ensure you manage accordingly.

  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead">Silver BlogStop explaining black box machine learning models for high stakes decisions and use interpretable models instead

    The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.

  • Seven Myths About the True Costs of AI Systems

    While there is much excitement today around implementing AI at the enterprise level, the financial costs of this process are often unexpected and underappreciated. These seven myths are crucial lessons learned that executives should know before heading down the road to AI.

  • Addressing the Growing Need for Skills in Data Science

    To address the current difficulties in hiring data scientists due to their short supply, many companies can benefit from retraining existing analytically minded employees.

  • How AI will transform healthcare (and can it fix the US healthcare system?)">Silver BlogHow AI will transform healthcare (and can it fix the US healthcare system?)

    This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.

  • Data Driven Government – Speakers Highlights

    The lineup of experienced, thought-leading speakers at Data Driven Government, Sep 25 in Washington, DC, will explain how to use data and analytics to more effectively accomplish your mission, increase efficiency, and improve evidence-based policymaking.

  • Domain-Specific Language Processing Mines Value From Unstructured Data

    Processing unstructured text data in real-time is challenging when applying NLP or NLU. Find out how Domain-Specific Language Processing can also help mine valuable information from data by following your guidance and using the language of your business.

  • Platinum BlogThe Death of Big Data and the Emergence of the Multi-Cloud Era">Gold BlogPlatinum BlogThe Death of Big Data and the Emergence of the Multi-Cloud Era

    The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.

  • Aliya: Data Scientist [New York, NY]

    Seeking a data scientist to be responsible for assisting with the development of Aliya’s state-of-the-art, advanced analytical machine-learning-based software framework that can ingest, structure and model large amounts of data in different formats and from different sources.

  • Top 8 Data Science Use Cases in Construction

    This article considers several of the most efficient and productive data science use cases in the construction industry.

  • Customer Churn Prediction Using Machine Learning: Main Approaches and Models

    We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.

  • Intuit: Sr Manager Data and Analytics – Speech and Text Insights [Mountain View, CA]

    Seeking a Sr Manager Data and Analytics for Speech and Text Insights, to oversee the design, development, deployment and management of batch and real-time fraud and credit risk models for both onboarding and monitoring purposes.

  • Intuit: Business Operations Analyst [Mountain View, CA]

    Seeking a Business Operations Analyst, a creative problem solver with a passion for innovation, technology and teams to help us revolutionize the way small businesses run their business and deliver on our mission of “powering prosperity around the world.”

  • Lower Rates End Friday for Mega-PAW Vegas – the Largest Predictive Analytics World to Date

    Five Predictive Analytics World Events in Las Vegas, Jun 16-20: Business, Financial, Healthcare, Industry 4.0, Deep Learning. Regular Pricing Ends This Friday. Register now!

  • MassMutual: Data Architect [Boston, MA, or Springfield, MA]

    Our ideal Data Architect is experienced working in an Agile development environment and has an understanding of SDLC. You have at least seven years’ experience in enterprise, application, and information data architecture design.

  • Uber’s Case Study at PAW Industry 4.0: Machine Learning to Enforce Mobile Performance

    Data scientists, industrial planners, and other machine learning experts will meet at PAW in Las Vegas on June 16-20, 2019 to explore the latest trends and technologies in machine & deep learning for the IoT era.

  • U. of Cincinnati Analytics Summit 2019, April 1-3

    Analytics Summit 2019 focuses on analytics and data science content to support the growth and development of analytics efforts in business, government and non-profit organizations.

  • Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019

    We recap some of the major highlights in data science and AI throughout 2018, before looking at the some of the potential newest trends and technological advances for the year ahead.

  • How AI can help solve some of humanity’s greatest challenges – and why we might fail

    AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short.

  • DATAx Singapore – meet Data Science Leaders – special year of the Pig offer

    At DATAx Singapore join data science and business leaders across industries presenting how machine learning algorithms and analytics improve business results. Book by 15 Feb and get 20% off using code KDNY20.

  • 2019 INFORMS Business Analytics Conference: Industry 4.0, Apr 14-16, Austin

    The 2019 INFORMS Conference on Business Analytics and Operations Research offers a rich experience for Analytics professionals and other business leaders. Get early rates until March 4.

  • Five Ways Your Safety Depends on Machine Learning

    Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.

  • Voloridge: Quant Data Analyst [Jupiter, FL]

    We are seeking an enthusiastic, self-motivated Data Services Analyst to work collaboratively with our data scientists and our operations team to meet the Voloridge mission of delivering superior risk-adjusted returns using proprietary modeling techniques.

  • Interactive Brokers Group (IBG): Data Scientist [Greenwich, CT]

    The successful candidate will transform information into insight to drive our continued growth, and to understand how we can service our clients better. The data scientist role combines internal and external sources of data to paint a clear picture of who our clients are, and how they engage with our products and services.

  • The 2019 PAW Business Agenda is Live – Super Early Bird expires this Friday

    Breaking News: The 2019 PAW Business program is live! Predictive Analytics World for Business is coming to Caesars Palace in Las Vegas, Jun 16-20, 2019. Super Early Bird ends this Friday!

  • Cummins: Advanced Analytics Systems Architect Principle [Columbus, IN]

    Cummins is seeking a Advanced Analytics Systems Architect Principle in Columbus, IN, to be accountable for the Architectural design of Advanced Analytics System inclusive of the Data Engineering, Data Science, Compute infrastructure and BI/Visualization and reporting components.

  • Linking Data Science Activities to Business Initiatives Using the Hypothesis Development Canvas

    The Hypothesis Development Canvas is an effective and concise tool that integrates the different elements of the “Thinking Like A Data Scientist” process into a single document.

  • Top 5 domains Big Data analytics helps to transform

    Big data analytics gives a competitive advantage to companies across many industries, especially, financial services, e-commerce, aviation, transportation, logistics, and energy. It enables to reduce downtime, mitigate risks, cut costs, and improve performance.

  • UnitedHealth Group: UHC Digital Project Manager [Minnetonka, MN]

    UnitedHealth Group is seeking a UHC Digital Project Manager in Minnetonka, MN. Join us and put your advanced knowledge and skills to work guiding project scope, time frames, funding needs, staffing requirements, allotment of resources and more.

  • Self-Service Data Prep Tools vs Enterprise-Level Solutions? 6 Lessons Learned

    A detailed comparison between self-service data preparation tools and enterprise-level solutions, covering business strategy, accessible tools and solutions and more.

  • UX Design Guide for Data Scientists and AI Products

    Realizing that there is a legitimate knowledge gap between UX Designers and Data Scientists, I have decided to attempt addressing the needs from the Data Scientist’s perspective.

  • Data Science at Northwestern

    Northwestern’s MASTER OF SCIENCE IN DATA SCIENCE is a fully online, part-time program that helps students build essential analysis and leadership skills for today's data-driven world. Apply now!

  • How to Organize Data Labeling for Machine Learning: Approaches and Tools

    The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.

  • Top 7 Data Science Use Cases in Finance

    We have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.

  • Operational Machine Learning: Seven Considerations for Successful MLOps

    In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives

  • Online Master’s in Data Science from Northwestern

    Build essential technical, analytical, and leadership skills for today's data-driven world in Northwestern’s online MASTER OF SCIENCE IN DATA SCIENCE program.

  • Top 10 Technology Trends of 2018">Gold BlogTop 10 Technology Trends of 2018

    In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

  • Topnotch Keynotes at PAW Healthcare, and 3 other Predictive Analytics Worlds in Vegas

    Join us in Las Vegas, June 3-7, 2018 and be there to witness these industry heavyweights share their knowledge in can't miss keynote sessions. Register by April 27 to save hundreds with current regular rates.

  • Data Skills: They’re Not Just for Data Scientists

    The continued growth of big data, both in terms of quality and accessibility, is disrupting a wide range of roles. The skills needed to analyse this data need to be learned by everyone - not just data scientists.

  • KDnuggets™ News 18:n11, Mar 14: Two sides of getting a job as a Data Scientist; 5 things to know about Machine Learning

    Also 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning; Great Data Scientists Don't Just Think Outside the Box; Favorite Data Science / Machine Learning Blog; Text Processing in R.

  • Justice Can’t Be Colorblind: How to Fight Bias with Predictive Policing

    Predictive policing uncovers racial inequity, which it threatens to perpetuate - but, if we turn things around, it also presents an unprecedented opportunity to advance social justice.

  • How To Grow As A Data Scientist">Silver BlogHow To Grow As A Data Scientist

    In order for a data scientist to grow, they need to be challenged beyond the technical aspects of their jobs. They need to question their data sources, be concise in their insights, know their business and help guide their leaders.

  • Northwestern’s MS in Data Science

    Northwestern’s MASTER OF SCIENCE IN DATA SCIENCE is a fully online, part-time program that helps students build essential analysis and leadership skills for today's data-driven world.

  • Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions

    Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.

  • Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018">Gold BlogData Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018

    The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.

  • The Qualitative Side of Quantitative Research

    Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.

  • Data Scientist: The Hottest Job on Wall Street

    The demand for professionals that can build financial analytics programs is booming. We foresee two main objectives- to predict market movement for profit, and to protect customer assets of banks.

  • Your Complete Guide to Predictive Analytics World – Oct 29-Nov 2 in New York City

    Predictive Analytics World for Business is slated for Oct 29-Nov 2 in New York City. See for yourself precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling and machine learning, and the kind of business results they achieve.

  • Corios: Consulting Manager

    Seeking a detail-oriented consultant who enjoys identifying and resolving project issues, making sure the project progresses on schedule and on budget, taking accountability for the project’s success.

  • How to Choose a Data Science Job

    All Data Scientists worth their salt should know the importance of working with facts rather than hunches. That’s why in the following article we’ll throw light on how five emerging roles yield a proven value that companies cannot ignore.

  • Using Machine Learning to Predict and Explain Employee Attrition">Silver BlogUsing Machine Learning to Predict and Explain Employee Attrition

    Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.

  • Top 10 Videos on Machine Learning in Finance">Silver Blog, Sep 2017Top 10 Videos on Machine Learning in Finance

    Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!

  • Are physicians worried about computers machine learning their jobs?

    We review JAMA article on “Unintended Consequences of Machine Learning in Medicine” and argue that a number of alarming opinions in this pieces are not supported by evidence.

  • Four Deep Track Themes at Predictive Analytics World, Oct in New York

    Predictive Analytics World for Business New York’s (Oct. 29-Nov. 2) rich program of brand name case studies and industry leaders covers deployed machine learning — across these topics: business, tech, marketing, and case studies.

  • What Is Optimization And How Does It Benefit Business?

    Here we explain what Mathematical Optimisation is, and discuss how it can be applied in business and finance to make decisions.

  • When Data Science Is Not Enough: Deriving Signal from Maritime Observations

    We examine the limits of "data science-first" thinking - letting technical skills drive the analysis, and only later adding domain understanding.

  • Hoag Hospital: Strategic Analytics Analyst Lead

    Seeking a Strategic Analytics Analyst Lead to partner with internal and external clients to deliver data driven insights that support decision making throughout the organization.

  • Celgene: Director, Big Data Ops Lead

    Seeking a Big Data DevOps Lead. The role will establish and manage the operational services necessary to ensure proper management of the platform health and to support ongoing use of the platform for business insights generation.

  • How GDPR Affects Data Science">Silver Blog, July 2017How GDPR Affects Data Science

    Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a “right to an explanation” for automated decision-making, and accountability for bias and discrimination in automated decisions.

  • Spotlight on the Remarkable Potential of AI in KYC (Know Your Customer)

    Most people would have heard of the headline-making tremendous achievements in artificial intelligence (AI): Systems defeating world champions in board games like GO and winning quiz shows. These are small realizations of AI, but there is a silent revolution taking place in other areas, including Regulatory Compliance in Financial Services.

  • Aetna: Principal Data Scientist

    Seeking a Principal Data Scientist, to be be responsible for leveraging advanced statistical predictive modeling to evaluate scenarios and make predictions on future outcomes. Analyzes very large data sets in real time databases and develops and implements mathematical approaches.

  • The Artificial ‘Artificial Intelligence’ Bubble and the Future of Cybersecurity

    What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.

  • Argus: Media Measurement & Targeting Analytics Manager

    Seeking a candidate to manage key product suite for media measurement and targeting and to develop data driven solutions for media measurement and targeting, while taking into consideration existing data challenges and restrictions.

  • Hadoop is Not Failing, it is the Future of Data

    The author disagrees with a previous KDnuggets post on “Why Hadoop is Failing” and argues that the Darwinian Open Source Ecosystem ensures Hadoop is a robust and mature technology platform .

  • Models: From the Lab to the Factory

    In this post, we’ll go over techniques to avoid these scenarios through the process of model management and deployment.

  • KDnuggets™ News 17:n15, Apr 19: Forrester vs Gartner on Data Science/Analytics Platforms; 5 Machine Learning Projects You Can No Longer Overlook

    Also Top mistakes data scientists make when dealing with business people; New Online Data Science Tracks for 2017; Cartoon: Why AI needs help with taxes.

  • Is Blockchain the Ultimate Enabler of Data Monetization?

    Is blockchain the ultimate enabler of data and analytics monetization; creating marketplaces where companies, individuals and even smart entities (cars, trucks, building, airports, malls) can share/sell/trade/barter their data and analytic insights directly with others?

  • What Is Data Science, and What Does a Data Scientist Do?">Gold Blog, Mar 2017What Is Data Science, and What Does a Data Scientist Do?

    This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.

  • Aetna: Principal Data Scientist

    Seeking a Principal Data Scientist to provide strategic leadership for the development, validation and delivery of algorithms, statistical models and reporting tools, and to act as the analytic team lead for highly complex projects.

  • 89degrees: Enterprise Data Architect

    Seeking an Enterprise Data Architect, to serve as the lead technical resource in the strategic oversight and planning of data models and database structural design and development.

  • Aetna: Principal Data Scientist

    Seeking a Principal Data Scientist in our Hartford, CT, Wellesley, MA, or New York, NY location, to be responsible for leveraging advanced statistical predictive modeling to evaluate scenarios and make predictions on future outcomes.

  • Aetna: Principal Data Scientist

    Seeking a Principal Data Scientist in our Hartford, CT, Wellesley, MA, or New York, NY location, to be responsible for leveraging advanced statistical predictive modeling to evaluate scenarios and make predictions on future outcomes.

  • Big Data to Big Profits: Strategies for Monetizing Social, Mobile, and Digital Data with Data Science, Mar 23-24, San Francisco

    This course will examine how firms can take big data to big profits through data monetization strategies and the best use of data science for growth and innovation across your organization.

  • Industry Predictions: Key Trends in 2017

    With 2017 almost upon us, KDnuggets brings you opinions from industry leaders as to what the relevant and most important 2017 key trends will be.

  • IDentrix: Data Scientist

    Seeking a passionate Data Scientist with a proven record of building data driven solutions, who is interested in data mining and modeling specialized large and connected datasets.

  • The Data Science Delusion

    Gleanings from observed technical misunderstandings between business leaders and data scientists (and among data scientists themselves) so dramatic that one could start wondering whether there is something wrong with data science as it is being practiced.

  • Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part 2

    Read the second and final part of this overview of the CDO Toolkit, which integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources.

  • Do You Suffer From Analytic Personality Disorder (APD)?

    Read this lighthearted take on Analytics Personality Disorder, a (nonexistent) syndrome for those obsessed with analytics.

  • Learn How to Turn Big Data into Big Profits: Northwestern MS in Analytics Executive Education Course

    Join Northwestern University's Master of Science in Analytics for an upcoming Executive Education Course, March 23 - 24, 2017 in San Francisco: Big Data to Big Profits.

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