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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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Strong foundational understanding of Big Data ecosystems to make trade-off decisions while developing end to end solutions on Cloud/On-Prem/Hybrid Data tech stackExperience balancing new product development with maintaining day-to-day data reporting operations in a very large scale enterprise set upExperience applying both qualitative analysis and quantitative analysis techniques and familiarity with common data science models and approaches to problem-solving.
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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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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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Experience with financial datasets (e.g. Refinitiv, S&P, Bloomberg) is a big plus. 2+ years of experience in a data engineering/science role OR a degree in data science or a similar discipline.
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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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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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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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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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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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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 big hands on financial services jobs Title: scientist in New York, NY
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