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Communicate the ML models and methods in a logical and understandable manner for non-analytical business users and model governance teamsYOUR QUALIFICATIONSMaster’s or PhD student in a quantitative research field such as: Statistics, Econometrics, Computer Science, Demography / Actuarial Science, Mathematics, Data Science, Quantitative Social Sciences, Epidemiology, Engineering, etc.
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Proficiency in data analytics tools and programming languages (e.g., Python, R, SQL, Power Platform, Alteryx). Stay up-to-date with emerging trends and best practices in data analytics, automation, and related technologiesWhat you'll bring:Bachelor's or advanced degree in Data Science, Analytics, Computer Science, or a related field.
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Work with our data engineering teams, data science teams, and analytical business users to review and ensure best practices are followed and high-quality code gets implemented. Familiarity with SQL and programming languages such as Python, Spark, YAML, and Bash.
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Proficiency in scripting languages such as R, Python, SQL, or Matlab. Assist in the interpretation and analysis of various geologic data including boring logs, geophysical wireline logs, geophysical surveys, and more.
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Knowledge of Power BI, SQL, Python, R, DAX, Databricks, Power BI Paginated Report Builder, and DevOps. Experience in U.S. electricity markets, wind farm operations, production-based availability, performance loss calculations, and high frequency timeseries data.
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Proficiency in SQL, DataBricks, Pandas, and statistical programming (Python and/or R). Mid-level to senior level experience in data science and experimentation, preferred.
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Using tools like SQL, Microsoft Excel, Essbase, Snowflake, PowerBI, Python, etc. Solid knowledge of P&C insurance products, pricing, underwriting, marketplace and product management, with understanding of product P&L, drivers of growth and profitabilityAbility to drive collaboration and teamwork with other functions and external partners at various levels of the organizationAdvanced research (quantitative and qualitative), statistical, analytical, processing, data science, data visualization, and mathematical skills.
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Exceptional skills with analytis programming languages and Business Intelligence Tools (SQL, R, Python, Qlik, SAP BO, SAS)Expertise in conducting deep-dive analyses which result in analytics insights and recommendations delivered via powerful, user-friendly, visualizations.
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Troubleshoots data-related issues using SQL and other scripting / high-level languages (e.g., R, Python) Specialized in implementing ELT applications using Snowflake and Azure cloud (Data Factory, Storage containers, On-premises data gateways, Integration Runtime environments, Hub-spoke network architecture etc.
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Experience with languages and tools such as Python, SQL, SAS, Qlik, Tableau, etc. Advanced skills in data wrangling, data engineering, data science, or related areas.
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Data science, data analysis, statistics, or related field, Master's degree in Business, Computer Science, Engineering, Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field, Related industry experience (for example, retail, merchandising, healthcare, eCommerce), Successful completion of assessments in data analysis and Business Intelligence tools and scripting languages (for example, SQL, Python, Spark, Scala, R, Power BI, or Tableau.
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Conduct functional, integration, regression, and performance testing of database and Datastores using industry standard technologies - SQL, Python, Great Expectations & Custom data validation frameworks if any.
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Proficiency in Excel, SQL, Python, R, SAS, Tableau, and other analytical tools. Qualifications:Degree in Data Science, Statistics, or a related field. Set up dashboards and data visualization tools.
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Experience analyzing data with technologies like Splunk, ELK, Hadoop, Python, or SQL. Technical experience in some of the following areas: Azure, AWS, SaaS, CAASM, SASE, SSE, IAM, EDR, Suricata, Zeek, Full Packet capture technologies, Firewall, Proxy, and Sandbox technologies.
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Must have strong technical skills in areas such as statistics, programming languages like R or Python, SQL (Structured Query Language), data visualization, and data cleaning and preparation.
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