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Data Integration/ETL Lead with hands-on experience in designing and implementing data warehouses and data marts for large financial institutions. 3 year of experience in Banking industry specifically in Financial Crimes Data Governance for Sanction Screening, Anti Money Laundering and KYC.
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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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Hands on experience with programming for extract transform operations and data analysis using languages such as, but not limited to R, Python, SQL, and Excel VBA. Experience in developing data visualization and business intelligence solutions using tools such as, but not limited to, Shiny, Tableau, PowerBI, and front-end technologies and languages.
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3+ years of hands-on experience as a quantitative developer developing financial models and software solutions. Experience working with financial data sets like Factset and Bloomberg a plus.
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Required Skills: 10+ years professional work experience as a data scientist or on advanced analytics / statistics projects with 5+ years’ experience in one of the following sectors: Aerospace & Defense, Automotive, Banking, Consumer Products & Retail, Financial Services, Healthcare, High Tech, Industrial Products, Insurance, Life Sciences, Manufacturing, Public Sector, Telecom, Media & Entertainment, and Energy & Utilities.
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Hands on experience in data engineering desired with extensive knowledge of SQL and PySpark, on Databricks Platform. Ability to design cloud and on premise data solutions and provide guidance across storage, big data platform services, Big Data ecosystem, vendor SaaS integrations, RDBMS and NoSQL databases is a plus.
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Develop your professional skills through one-on-one mentoring from a Data Science Lead assigned to you over the summer. Current PhD Student in Quantitative Discipline related to Data Science, including but not limited to Statistics, Mathematics, Economics, Computer Science, etc.
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Basic Qualifications:Minimum of 5+ years of product management experience with strong focus on building and delivering successful data products, services and capabilitiesBachelor’s degree in a relevant field required, such as Engineering, Math, Science, Economics, etc.
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Troubleshooting and debugging data-related issues on the AWS Databricks platform. CompanyFederal Reserve Bank of New YorkWorking at the Federal Reserve Bank of New York positions you at the center of the financial world with a unique perspective on national and international markets and economies.
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Minimum 5 years’ experience in data engineering with a proven track record in the Azure ecosystem and with Azure DevOps. Minimum 5 years’ experience with Data Modeling, ETL, and Data WarehousingMinimum 3 years’ hands on experience with Azure Services.
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Requirements: Requires a Master’s in Statistics, Computer Science, Data Science, Machine Learning, Applied Math, Operations Research, Economics, or a related field plus two (2) years of experience as a Data Scientist, Data Engineer, or other occupation/position/job title involving research and data analysis.
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You will own and be responsible for leading the product development lifecycle for services that support online and offline use cases, cross-domain and historical time-series data. PREFERRED QUALIFICATIONS- Experience working directly with Engineers on product enhancements- Experience in project management methodologies, business analysis, or process improvement- Experience owning data-centric products.
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Hands-on experience with dbt for data modeling and transformations. 5+ years of experience in data engineering, with a strong focus on AWS, Redshift, and Snowflake.
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5-10 years of experience in areas related to credit risk reporting and data analytics in financial institutions or consulting. VP, Portfolio Management Data Science. Will lead initiatives to enhance processes and infrastructure, leading the implementation of data science initiatives, applying expertise in develop monitoring reports, dashboards, and predictive models to enhance risk assessment capabilities.
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We work closely with other Treasury-Finance teams (Liquidity Management and Capital Markets), Stripe's Information team (Data Science and Data Engineering), Stripe's Money Movement & Storage (MMS) product and engineering teams to build great capital and financial risk infrastructure.
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data science hands on financial services jobs Title: scientist in New York, NY
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