Lead Data Scientist

Key Skills

Java Programming, Python Programming, .NET, English Proficiency, Azure, AWS, Big Data Technology, Machine learning techniques, TensorFlow, D3.js, Google Cloud Platform (GCP), GraphQL, CUDA/GPU programming, AI technology, R Programming, Clustering, Databricks, SPARQL, Neural Networks, Statistical modeling, Data Architecture, Large data sets, Decision trees, Effective communication skills

Job Description

The Lead Data Scientist is a critical position for understanding of the current state of product performance, quality system performance and regulatory compliance standards. The Data Scientist will work with key stakeholders to identify opportunities for leveraging company data to drive analytical solutions. The Data Scientists will be responsible for the for designing and building computational models, discovering insights and identifying opportunities through the use of statistical and algorithmic methods (for instance machine learning) as well as defining data visualization techniques.

The Lead Data Scientist is an expert in using advanced analytical and technical skills to convert raw data into actionable business insights as well as creating, evaluating and implementing data science solutions. This includes data preparation, data visualization, exploratory analysis and software development related to data science.

The Lead Data Scientist develops and achieves discipline strategy in alignment with functional goals and key business objectives. Advances technical expertise within function. Proactively demonstrates leadership by advising and sharing knowledge and expert opinions. Mentors and trains on the use of statistical techniques and assesses current and emerging business challenges enabling functional goal achievement.

Data scientists will work with the Global Process Owners to define key metrics and targets for their Quality System Element.

Primary Responsibilities:

In accordance with all applicable federal, state and local laws/regulations and Corporate BD, procedures and guidelines, this position is responsible for:

• Understand business problems and design end-to-end data science use cases
• Collaborate across the function to understand data, IT and business constraints
• Prioritize, scope and measure the corresponding Key Performance Indicators (KPIs)
• Collaborate with developers to implement and deploy scalable visualization solutions
• Establish best data operational practices and maintain all compliance requirements
• Establish the monitoring of data science models in production
• Lead agile approach to initiatives and launches
• Apply strong expertise in data science to design, prototype, and build the next-generation analytics engines and services
• Guide the organization about the business potential and strategy of artificial intelligence (AI)/data science
• Actively network on a regular basis with domain experts to better understand the business mechanics that generated the data
• Train and coach other business and IT staff on basic data science principles and techniques
• Actively network on a regular basis with internal and external partners
• Promote collaboration and knowledge exchange with other data science teams within and outside the organization
• Provide thought leadership by researching best practices, conducting experiments, and collaborating with industry leaders
• Work closely with functions across BDX to implement models and monitor outcomes
• Develop processes and tools to monitor and analyze model performance and data accuracy

Supervision and Budgetary Responsibility:

• N/A

Education and Experience:

• Requires a minimum of a BS/MS degree in applied mathematics, engineering or other relevant discipline. Graduate degree preferred.
• Five-plus (8+) years of relevant work experience in data science.
• Experience in a pharmaceutical, medical device or other regulated field (GMP mainly, GLP, GCP)
• Advanced statistical techniques and concepts and experience with applications
• Knowledge of statistical computer languages to manipulate data and draw insights from large data sets
• Ability to quickly develop extensive domain knowledge in various topics
• Experience with a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• English working proficiency and communication skills (verbal and written)
• Experience presenting data and analysis to upper management.

Technical Skills:

• Solid working knowledge of Python, R, HQL, DataBricks, Tensorflow, SPARQL, D3JS.
• Knowledge of data architecture, big data architectures, experience of building solutions on Azure, AWS, GCP, Multi-threaded data processing on CPU and GPU architectures.
• Experience of Programming Languages such as .Net and/or Java (Scripting), API and Webservices.
• RDF, Triplestore and Graph databases.
• Travel may be required on a limited basis - less than 20%.