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Deliver datasets from onboarding through mapping and automated DQ so they can be handed off to the Data Operations team for DQ maintenance and consumed by research teams and production. The Asset Management Quant Research Engineering team is looking for a Director of Data Engineering who wishes to take on the challenge of building a world-class data operation.
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This entails designing and implementing a sophisticated data governance framework spanning across capabilities and tools such as data quality checks, defining/tracking KPI’s, data lineage and data structure and organization (e.g. organized data hierarchies, well-maintained data dictionary and report catalogue, as well as data and report certification.
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Develop and execute strategies that maximize the value of data science applications (e.g. in AI/ML, Computational Chemistry, and Biology). Moreover, this embedding with DSI nurtures fertile Research and Development collaborations that leverage DSI’s excellence in quantitative pharmacology, biostatistics, and big data.
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With a bridging seat on the leadership team of DSI, the Head of Computational Science & Data Strategy, Research, ensures a seamless connection with the data infrastructure and governance responsibilities of DSI and its responsibility for enterprise data solutions in partnership with Takeda’s corporate Data & Technology function.
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The person will work directly with stakeholders across - ESG Investments, ESG Product Strategy, ESG Data and Operations, Client, Product and Regulatory Reporting, and Enterprise Risk Management to understand their data and reporting requirements and to translate requirements into functional specifications for technology partners building/ enhancing IT solutions for ESG reporting.
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Help implement data governance policies wherever the data is accessed (Databricks, Snowflake, Spotfire). Azure Data Factory, Databricks (PySpark) and Snowflake preferred. Familiarity with both batch and streaming data operations.
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Develop/ Build and enhance data pipelines using Python, Spark based data engineering solutions (Databricks), and SQL (AWS Redshift). Hands on experience working in cloud data platforms such as AWS Redshift, Spark based data engineering solutions.
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Experience with modern columnar data warehouses such as Snowflake, Redshift, BigQuery. Driving Results: Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Python and/or Node.js, SQL (using dbt for transformation), commercial SaaS and OSS offerings, and AWS ‘big data’ technologies.
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Director, Quant Data Engineering. 7+ years of data engineering experience, including in senior and team leadership roles, at peer firms utilizing agile methods and modern SDLC processes to deliver quality technological solutions in a transparent, reliable way.
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You may have experience with Spotfire, Tableau or other data visualization platforms. You bring previous experience with proteomics data processing and interpretation. Perform exploratory analysis using data science techniques on structured and unstructured datasets and develop algorithms for early drug discovery efforts.
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Familiarity with GCP, ICH and FDA requirements as applicable for Clinical Data Management. The successful candidate will provide clinical data management oversight of outsourced clinical trials including but not limited to project management, vendor management, coordination of internal reviews, and approval of deliverables.
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Advanced knowledge of math, statistics, multivariate analysis, statistical tests Advanced Python coding skills Experience with one or more machine learning frameworks such as TensorFlow and/or PyTorch Understanding of numerical implementations Understanding of cloud computing Knowledge of data querying knowledge of EMG, EEG, ECG, IMU, CV algorithms Experience with C.
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The role of an HR Data Visualization & Governance Analyst is essential to the HR Reporting and Analytics team. Utilize HR data and analytics expertise to impact business decisions, establishing an HR competitive advantage for our diverse retail brands, supply chain, and shared services customers.
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The successful candidate will join the Model Risk Management and Validation Group (MRM&V).
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Experience with at least one Data Integration tool on the level of Dell Boomi, DataStage, or Informatica. The University IT Services (UITS) Enterprise Data and Analytics (EDA) team is responsible for developing and managing shared services in the area of data and analytics for the entire UMass university system.
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data job Company: Home Depot in Boston, MA
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