SENIOR/PRINCIPAL DATA SCIENTIST - FINANCE

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

As a Senior/Principal Data Scientist in Finance you will partner to create actionable insights and models, and help apply them to drive decisions and build value across the business. Finance plays a central role in our mission to provide better outcomes to patients faster. We partner locally and globally across the business, including with Sales and Marketing, Research, Clinical, Medical, Manufacturing and other core business units to augment decision making, reduce cost and increase revenue. Your main focus will be in data science and applied AI, in a team where the scope of activities includes data operations, analytics, data science, at-scale ML, automation and AI. Having a high level of technical proficiency is important, and beyond that your business acumen and teamwork skills will allow you to work closely with a skilled team of data scientists, engineers, analysts and business partners.

You will be effective working in teams and integrating data science and AI into analytics and business practice, and embedding predictive models and visualizations into data-centric products and processes that run at scale. You will be a respected member of a vibrant data science community of thousands, and in project teams at stages ranging from planning to at-scale operations. You will work in Finance and as a partner across the scope of the business. This is an opportunity to make contributions that improve patient health, while working with a strong team at a company that provides great benefits and an excellent work environment.

Minimum Requirements:

• PhD with 2+ years of industry experience as a data scientist, or a Master’s degree with thesis work and 4+ years of experience, in Computer Science, Computational Biology/Chemistry, Engineering, or similar areas.
• Industry experience in team settings with strong demonstrated contributions as shown by attributable revenue generation, cost savings, quality product releases, patents, publications and conference presentations.

Desired Experience:

• The successful candidate will have a mixture of technical and organizational experience. Technical expertise desired includes:
• Operating in data environments with multiple complex data sources: finding, accessing, cleansing, merging, validating and annotating data.
• Performing data exploration - including finding trends, key features, feature engineering, discovering and showing relationships and developing visualizations.
• Developing, validating predictive models will include familiarity with test and training sets, supervised and unsupervised methods, and understanding strengths and weaknesses of algorithms (such as clustering techniques, Random Forests, Gradient Boosting etc.).
• Characterizing the accuracy of models across use cases, creating benchmarks, developing unit tests, bringing models to production deployment, monitoring usage and accuracy of models in production, refreshing models, and phasing the updated model into production in frameworks such as A/B testing is desired.
• Automating data processes and data quality metrics to detect and account for irregularities in data quality.
• Communicating key findings within data science, in cross-disciplinary project teams, and to partnered groups across the business with different needs and perspectives (such as Commercial, Research and Manufacturing).