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Job Description/Role Profile for Senior Data Scientist

Objectives and Purpose

• The Senior Data Scientist is responsible for applying expertise and best practices in full-stack data science capabilities including advanced data analytics, statistical modeling (AI/ML), MLOps, data engineering, and data visualization to develop data-driven solutions to enable business insights. This individual reports to the Lead Data Scientist and partners closely with the Data Engineering team to gather accurate datasets of analysis and modelling needs.
• The Senior Data Scientist will:
  • Partner with the business and other key stakeholders to understand the current state and identify opportunities to transform the business into a data-driven organization.
  • Translate processes and requirements into analytics solutions and metrics with effective data strategy, data quality, and data accessibility for business decisions.
  • Apply the appropriate quantitative techniques to provide the business with data-driven, actionable insights through user-friendly analytics models to evaluate “what-if” scenarios and enable smarter decision-making.
  • Evaluate current data, analytical models, and experiments, on an ongoing basis, to validate hypotheses and confirm they provide business value as requirements and objectives evolve.

Your key responsibilities

Data Science

• Be a trusted advisor and partner to the business by identifying analytical improvement opportunities based on defined pain points, problem statements, scope, and analytics business case.
• Develop customer-centric solutions with recommended data model and business intelligence (BI) technologies.
• Lead the development and implementation of advanced analytical models and methods.
• Design, execute, interpret, and validate statistical and predictive models.
• Support building a culture of innovation by strategically applying disruptive/emerging technology (i.e., generative AI).
• Develop forecasting models to process and analyze large quantities of data to identify key trends and business insights.
• Create data visualizations that are detailed, clear, multi-faceted, and user-focused to effectively communicate a narrative.
• Understand, validate, and address requirements and needs for data science, modelling, and forecasting; following proper documentation protocol to track intake forms and solutioning processes.
• Extract, transform, and load data from one data source (e.g., Databricks) into a single, consistent data source for data modeling and workflow orchestration purposes (i.e., representations of data flows and relationships).

Relationship Building and Collaboration

• Integrate AI capabilities into traditional Data Science methodology, communicating the value to business partners and helping the business to make timely, actionable decisions.
• Support Lead Data Scientist and team members to define business requirements, participating in workshops and/or prototyping sessions focused on enhancing analytics product functionality.
• Integrate AI capabilities into traditional Data Science methodology, communicating the value to business partners and helping the business to make timely, actionable decisions.
• Collaborate with DevOps and Database Teams to ensure proper design of databases and integration with enterprise applications.
• Design data visualization solutions, with Enterprise Data and Analytics Teams, that synthesize complex data for data mining, discovery.
• Supervise, train, and mentor junior data scientists and analysts, creating a culture of continued learning and development.

Skills and attributes for success

Technical/Functional Expertise

• Advanced experience and understanding of current and emerging data, digital, and IT technologies (i.e., generative AI), as well as Analytics processes and service models.
• Proficiency in analyzing and interpreting large datasets using AI and machine learning techniques.
• Ability to leverage generative models to create synthetic data, simulate scenarios, or analyze and convert into actionable insights.
• Ability to identify actionable insights through data mining and pattern recognition from data and provide recommendations.
• Strong business acumen with knowledge of the Pharmaceutical, Healthcare, or Life Sciences sector is a plus.

Leadership

• Strategic mindset of thinking above the minor, tactical details and focusing on the long-term, strategic goals of the organization.
• Advocate of a culture of collaboration and psychological safety.

Decision-making and Autonomy

• Shift from manual decision-making to data-driven, strategic decision-making.
• Proven track record of applying critical thinking to resolve issues and overcome obstacles.

Interaction

• Proven track record of collaboration and developing strong working relationships with key stakeholders by building trust and being a true business partner.
• Strategize with IT Development Teams to develop a standard process to collect, ingest, and deliver data along with proper data models for analytical needs.
• Ability to work alongside intelligent machines and humanize data and insights.

Innovation

• Passion for re-imagining new solutions, processes, and end-user experience by leveraging advanced technologies (i.e., generative AI/ML), effective statistical models, and enterprise analytics platforms and tooling to support BI solutions and drive business results
• Advocate of leveraging intelligent machine learning/AI to effectively work alongside technology, humanize data and insights, and mature business capabilities
• Advocate of a culture of growth mindset, agility, and continuous improvement

Complexity

• Demonstrates multicultural sensitivity when collaborating
• Takes initiative to anticipate challenges and take proactive measures in addressing complex problems.

To qualify for the role, you must have the following:

Essential skillsets

• Bachelor’s degree in Data Science, Computer Science, Statistics, or related field
• At least 5+ years of experience of data mining/data analysis methods and tools, building and implementing models, and creating/running simulations
• Familiarity with AI libraries and frameworks
• Experience and proficiency in applied statistical modeling (e.g., clustering, segmentation, multivariate, regression, etc.
• Demonstrated understanding and experience using:
  • Data Engineering Programming Languages (i.e., Python, Pyspark)
  • Distributed Data Technologies (e.g., Spark, Hadoop, H20.ia, Cloud AI platforms)
  • Data Visualization tools (e.g., Tableau, R Shiny, Plotly)
  • Databricks/ETL
  • Statistical Model Packages (MLib/SciKit-Learn, Statsmodels)
  • GitHub
  • Excel
• Creating new features by merging and transforming disparate internal & external data sets
• Strong organizational skills with the ability to manage multiple projects simultaneously and operate as a leading member across globally distributed teams to deliver high-quality services and solutions
• Processes proficiency in code programming languages (e.g., SQL, Python, Pyspark, AWS services) to design, maintain, and optimize data architecture/pipelines that fit business goals
• Excellent written and verbal communication skills, including storytelling and interacting effectively with multifunctional teams and other strategic partners
• Demonstrated knowledge of relevant industry trends and standards
• Strong problem solving and troubleshooting skills
• Ability to work in a fast-paced environment and adapt to changing business priorities

Desired skillsets

• Degree in Data Science, Computer Science, Statistics, or related field
• Proven experience in developing and applying predictive modelling, deep-learning, or other machine learning techniques
• Experience in IICS/DMS (Data migration service)
• Experience in a global working environment
• Experience in solution delivery using common methodologies, especially SAFe Agile but also Waterfall, Iterative, etc.

Travel requirements

• Access to transportation to attend meetings

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