Assoc. Dir, Data Science Market Activation

**Associate Director, Data Science**

As an Associate Director in Data Science Market Activation, you will be focused to lead our data science team and drive the development and implementation of innovative data-driven strategies. This role is critical to shaping our data science initiatives, building advanced analytical models, and collaborating cross-functionally to enhance decision-making processes across the organization.

Associate Director is expected to lead a function of data scientist. Is accountable for business delivery, growth, value realization and strategy. Availability in office is MUST and Expected to lead on below aspects.

**Roles and Responsibilities**:
**1)**Functional R&R**
- Be thought leader to our Business Engagement Leads and Marketing team to understand the business problem and provide consultative and analytical solution
- Should have strong understanding of Pharma healthcare industry and therapy knowledge
- Design & build predictive models to forecast patient outcomes, such as readmission risk, disease progression, or response to treatment, enabling proactive care management.
- Identify & define HCP segmentation and targeting, micro segmentation by content, call planning
- Analyze longitudinal healthcare data to track patient journeys over time, providing insights into disease progression and treatment efficacy.
- Ensure data solutions are interoperable across different healthcare systems and platforms, facilitating seamless data exchange and collaboration.
- Collaborate with data engineering, data science & IT teams to deliver business friendly dashboard & reporting solutions
- Evaluate the effectiveness of treatments and interventions by analyzing patient outcomes data to inform clinical decision-making and improve care quality
- Should be hands on with Python, SQL, Databricks, Dataiku and other platforms. Should have product mindset to build process and analytical solution as enterprise products
- Provide thought leadership and data science best practices to the broader Data & Analytics organization
- Provide training & coaching to internal stakeholders to enable a self-service operating model
- Continuously innovative on best practices & technologies by reviewing external resources & marketplace

**2)**People Management R&R**- Leadership: Provide leadership, guidance, and mentorship to Senior Managers and Managers of analysts and data scientists. and create opportunities for professional growth.
- Team Culture: Foster a collaborative and inclusive team environment, encouraging knowledge sharing and professional growth, timely connect with associates and respect for opinion from all.
- Capability Building: Develop training programs and initiatives to enhance the skills and capabilities of team members in alignment to their career aspiration and organizational demand and scalability.
- Performance Management: Set clear performance expectations, conduct regular performance evaluations, and provide timely feedback to team members being assertive and clear.
- Career Development: Work with team members to define career goals and create development plans to support their advancement within the organization in alignment with their aspirations.
- Cross-functional Collaboration: Collaborate with other departments and teams to ensure alignment on analytics initiatives and projects
- Recognize Talent: Create platform for talent showcasing and visibility. Reward and recognizing for their contributions
- People Engagement: Engage with team members, create speak up and cohesive culture

**3)**Business Management R&R**- Strategy Development: Contribute to the development of the analytics strategy aligned with the overall business objectives.
- Project Management: Oversee the planning demand, execution, and delivery of analytics projects, ensuring they are completed on time and within budget.
- Insights Delivery: Ensure the delivery of actionable insights and recommendations to key stakeholders to drive business decisions.
- Data Governance: Establish and enforce data governance policies and procedures to ensure data quality, integrity, and security.
- Performance Measurement: Define key performance indicators (KPIs) and metrics to measure the effectiveness of analytics initiatives and track progress against business goals. Measure P&L of the function.
- Continuous Improvement: Identify opportunities for process optimization and efficiency gains within the analytics function.
- Risk Management: Identify and mitigate risks associated with analytics projects, such as data privacy and security risks.

**4)**Organization R&R**- Organization Vision: Understand organization and leadership to map team strategy accordingly.
- Stakeholder Engagement: Build and maintain relationships with key stakeholders across the organization to understand their analytics needs and priorities. Brand HHDDA services and support to help them scale business initiatives.
- Resource Allocation: Allocate resources effectiv