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Job Description for Lead Data Scientist
Objectives and Purpose

• The Lead 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 partners closely with Business Unit Leaders to model complex problems, derive analytical conclusions, and identify opportunities for improvement.
• The Lead Data Scientist will:
  • Apply strong expertise in artificial intelligence through use of machine learning, data mining, and information retrieval to design, prototype and build next generation advanced analytics engines and services.
  • Translate processes and requirements into analytical solutions and metrics, that can contribute towards data-driven solutions and strategies for the business.
  • Develop user friendly analytical models for the business to provide data driven actionable insights which would enable a more informed decision-making.

Your key responsibilities
Data Science

• Develop customer-centric solutions with recommended data model and business intelligence (BI) technologies.
• Create repeatable, interpretable, dynamic, and scalable statistical models that are seamlessly incorporated into analytic data products, ability to discover correlations between variables and generate predictions/forecasts of data-driven decisions.
• Extract, transform, and load data from one data source (e.g., Databricks) into a single, consistent data source for data modelling and workflow orchestration purposes (i.e., representations of data flows and relationships).
• Advocate and educate on the value of data-driven decision making with focus on “how and why” of solving problems.
• Oversee forecasting models that process and analyze large quantities of data to identify key trends and business insights.
• Review and refine data visualizations that are detailed, clear, multi-faceted, and user-focused to effectively communicate data insights throughout a narrative.

Relationship Building and Collaboration

• Collaborate with business partners to identify analytical improvement opportunities based on defined pain points, problem statements, scope, and analytics business case.
• Strategize with IT Development Teams to develop a standard process to collect, ingest, and deliver data along with proper data models.
• Lead team members in defining business requirements, facilitating workshops and/or prototyping sessions focused on enhancing analytics product functionality.
• Collaborate with internal and external partners to develop analytics that advance end-to-end Data Science solutions and practices.
• Coordinate with DevOps, Database Teams to ensure proper design of system databases and integration with enterprise applications.
• Design data visualization solutions, with Enterprise Data and Analytics Team, that synthesize complex data for data mining, discovery.

Skills and attributes for success

Technical/Functional Expertise 

• 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 Data Analysis and Visualization, analyzing and interpreting large datasets using AI and machine learning techniques.
• Understanding of AI concepts, algorithms, and machine learning models and the ability to apply AI technologies to solve business problems.
• Ability to leverage generative models to create synthetic data, simulate scenarios, or analyze outputs into actionable insights.
• Ability to identify actionable insights from data and provide recommendations.
• Strong business acumen with knowledge of the Pharmaceutical, Healthcare, or Life Sciences sector is a plus, but we also value perspective gained from other sectors.

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

• Play a lead role in decision-making processes by providing data-driven insights and solutions.
• 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.
• Lead analytical approaches, integrating work into applications and tools with data engineers, business leads, analysts, and developers.
• Demonstrated success in collaborating with different IT functions, contractors, and constituents to deliver technical solutions that meet Takeda technology standards and security measures.
• Ability to work alongside intelligent machines and humanize data and insights.
• Passion for teaming, coaching, and learning with a growing team of Data Scientists.

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 

• High multicultural sensitivity to effectively lead teams
• 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 10+ 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
• Advanced experience in developing and applying predictive modelling, deep-learning, or other machine learning techniques 
• Demonstrated understanding and 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
• Ability to fly to meetings regionally and globally
   

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