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At least 5+ years direct experience with pharmaceutical sales data and data management with the emphasis on syndicated data, Specialty Pharmacy and. Within our commercial Insights, Analytics, and Data organization we are transforming to better power decision-making across our end-to-end commercialization process, from business development to late lifecycle management.
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Enhance/Develop/Sustain current decision support tools or replacements along with internal and external portals using data/ tools such as Army VANTAGE, ADVANA, Microsoft Power Automate, data visualization tools (Tableau, Qlik, PowerBI), Python with data libraries of (PySpark, Pandas, NumPy), Structured Query Language (SQL), Statistical Analysis System (SAS), Oracle, Microsoft Azure, Microsoft Suite of products, machine learning methods, and other data analytics tools, as necessary.
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As the Data Scientist, you will use Google Cloud Platform (GCP) tools and technologies, such as Vertex AI and BigQuery, to build, deploy, and monitor scalable and robust machine learning solutions.
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The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) Bureau of Immunization (BOI) Citywide Immunization Registry (CIR) is a central record-keeping system established by DOHMH to track the immunization status of individual children and adults, monitor immunization levels in the population in a timely manner, and implement publicly purchased vaccine ordering, management, and accountability.
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We integrate off-the-shelf and new development efforts to sustain and enhance Defense Intelligence Agency’s (DIA) National Media and Exploitation Center (NMEC) architecture by leveraging cloud-based computing, artificial intelligence (Al), machine learning (ML) and cross-domain transfer systems to provide cutting edge data exploitation, enrichment, triage, and analytics capabilities to Defense and Intelligence Community members.
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Familiarity with the technology stack available in the industry for metadata management: Data Governance, Data Quality, MDM, Lineage, Data Catalog etc. Familiarity with the Technology stack available in the industry for data management, data ingestion, capture, processing and curation: Kafka, StreamSets, Attunity, GoldenGate, Map Reduce, Hadoop, Hive, Hbase, Cassandra, Spark, Flume, Hive, Impala, etc.
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Commodity Analytics helps commodity producers, processors, buyers, and traders across agriculture/softs, metals, energy, and consumer sectors improve commodity price risk capabilities with cutting-edge data science.
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In this role, your work on the team will primarily be in applying advanced analytics to enable better commodity risk management decisions. The Commodity Trading Service Line at McKinsey supports clients in commodity trading and risk strategy, trading operations transformation, and trading and risk digitization driven by deep trading experts with hands-on trading experience and advanced analytics assets.
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Strong understanding of master data management in commercial pharma across HCPs, HCOs and affiliations management (match merge rules, survivorship rules etc. Deliver working in global delivery model leveraging virtual teams in different time zonesTechnology LeadershipDevelop and maintain skills to the highest standards consistent with best practices and emerging technology trends in cloud data management, MDM, reporting and analytics.
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Undergraduate degree is required; advanced degree in a quantitative discipline such as computer science (especially machine learning), applied mathematics, economics, quantitative finance or engineering is preferred or equivalent practitioner experience.
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2+ years of commodity markets experience developing trading or hedging strategies (especially physical/cash markets) or price-discovery analysis in basic materials/metals, agriculture, softs, chemicals, plastics or oil & gas preferred.
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You'll work closely with McKinsey's Commodity Trading Service Line to support clients across sectors and geographies. For example, you might work as the lead in maintaining and expanding existing hedging strategies by re-training existing models through process driven approaches.
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Sidekick is an open source ETL framework for syncing data between SaaS tools (Salesforce, Zendesk, Notion, etc) to vector stores, a necessary step in building GPT powered applications. You ideally have a background in data engineering and should be just as comfortable making architectural decisions as implementing them in code.
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Overseeing or acting as intermediate between Data Management, Statistical Programming and external data provider regarding external data specifications, data, and technical issues.
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Strong knowledge with CDISC (CDASH/SDTM), medical dictionary coding standards, FDA and ICH guidelines, GCP, 21 CFR Part 11, Good Clinical Data Management Practices (GCDMP) Ability to development/oversee Data Management specific documentation including but not limited to the eCRF specifications, eCRF completion guidelines, edit check specifications, validation documents, DMP and DRP (Data Review Plan.
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Title: data management Company: Hoffmann La Roche
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