Associate Data Science Engineer

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The Data Science and Insights team is looking for an Associate Data Science Engineer who possesses strong quantitative skills and business acumen to work with stakeholders across the organization/clients and whose primary goals include building data products, models and infrastructure that support analytics and data science at scale. Our Data Science and Insights practice provides services and solutions across commercial sales operations, multichannel marketing, SFR, forecasting, HCP/ anonymized Patient level data and advanced prescriptive / predictive analytics. With business users all across the company, the team works cross-functionally to ensure reports, analytics, and models are supported by a stable, efficient, and accurate back end.

This is a full-time role with benefits.

Responsibilities:

  • Solve clients’ complex business problems and provide impactful business insights using advanced analytics, quantitative tools, mathematical and behavioral models, and modelling techniques.
  • Develop and conduct complex analytical processes and data integration on clients’ and internal data.
  • Work closely with the data warehouse/ product team to ensure the architecture supports algorithms that are reproducible for many clients while being able to tailor said models for individual clients.
  • Utilize exceptional oral and written skills to communicate insights to stakeholders (internal/external).
  • Demonstrate impeccable ethics and judgment when dealing with confidential data.
  • Develop, deploy, and support analytic data products, such as data marts, ETL’s (Extract/ transform/ load), functions (in Python/SQL/R), and visualizations.
  • Navigate various data sources and efficiently locate data in a complex data ecosystem.
  • Work closely with our data science and insights team to ensure production models are built using a scalable back end.
  • Maintain and support deployed solutions and data products.

Requirements:

  • BA/BS preferred in a technical or engineering field (Master’s preferred). Economics, Machine Learning, Applied Statistics, Applied Mathematics, Physics, Engineering, Computer Science, Operations Research, or other quantitative disciplines with at least 1+ year of relevant industry experience, or an equivalent M.S.
  • 1-3 years of experience in a data engineering or full-stack data scientist role.
  • Experience in data analysis techniques such as: classification, pattern recognition, clustering, feature analysis, NLP, fuzzy matching, sentiment analysis, A/B testing, active/adaptive learning.
  • Experience in areas of customer segmentation, ROI / impact analysis, Pathway’s analysis, channel and content preference modelling, text analytics.
  • Proficient in R or Python and/or C/C++ is preferable.
  • Experience working in cloud data platforms is preferable (Azure, AWS, Snowflake or similar).
  • Proficient in at least one of the visualization tools like Qlik Sense, PowerBI and/or Tableau preferable.
  • Relevant technical experience in machine learning, predictive modelling, advanced analytics, and statistics-related work. Experience with data analysis, visualization and workflow software is preferable.
  • Ability to manipulate large data sets and develop statistical models, and accurately determine cause and effect relationships.
  • Excellent SQL skills.
  • Excellent problem-solving and quantitative skills, including the ability to decompose issues, identify root causes, and recommend solutions.
  • Excellent oral and written communication skills with the ability to effectively explain complex problems and advocate technical solutions to other team members and clients.
  • Must be comfortable with changing requirements and priorities.
  • Must be results-oriented and able to move forward without complete information and with minimal supervision.
  • Experience in the pharmaceutical / life sciences / healthcare industry is preferable. Prior experience in one or more of relevant commercial areas (market research, digital marketing, market mix, commercial effectiveness) strongly preferred.
  • Travel may be necessary 10-15% of the time.

Contact:

Phil Portantino, Chief Human Resource Officer – philipp@cnxsi.com