Data Scientist

About Improzo

At Improzo (Improve + Zoe; meaning Life in Greek), we believe in improving life by empowering our customers. Founded by seasoned Industry leaders, we are laser focused for delivering quality-led commercial analytical solutions to our clients. Our dedicated team of experts in commercial data, technology, and operations has been evolving and learning together since our inception. Here, you won't find yourself confined to a cubicle; instead, you'll be navigating open waters, collaborating with brilliant minds to shape the future. You will work with leading Life Sciences clients, seasoned leaders and carefully chosen peers like you

People are at the heart of our success, so we have defined our CARE values framework with a lot of effort, and we use it as our guiding light in everything we do.  We CARE

· Customer-Centric: Client success is our success. Prioritize customer needs and outcomes in every action. 

· Adaptive: Agile and Innovative, with a growth mindset. Pursue bold and disruptive avenues that push the boundaries of possibilities.

· Respect: Deep respect for our clients & colleagues. Foster a culture of collaboration and act with honesty, transparency, and ethical responsibility.

· Execution: Laser focused on quality-led execution; we deliver Strive for the highest quality in our services, solutions, and customer experiences.

 About the Role:

We're looking for a Data Scientist in Pune to drive insights for pharma clients using advanced ML, Gen AI, and LLMs on complex healthcare data. You'll optimize Pharma commercial strategies (forecasting, marketing, SFE) and improve patient outcomes (journey mapping, adherence, RWE).

Key Responsibilities:

Data Exploration & Problem Framing:

• Proactively engage with client/business stakeholders (e.g., Sales, Marketing, Market Access, Commercial Operations, Medical Affairs, Patient Advocacy teams) to deeply understand their challenges and strategic objectives.
• Explore, clean, and prepare large, complex, and sometimes messy datasets from various sources, including but not limited to: sales data, prescription data, claims data, Electronic Health Records (EHRs), patient support program data, CRM data, and real-world evidence (RWE) datasets.
• Translate ambiguous business problems into well-defined data science questions and develop appropriate analytical frameworks.

 Advanced Analytics & Model Development:

• Design, develop, validate, and deploy robust statistical models and machine learning algorithms (e.g., predictive models, classification, clustering, time series analysis, causal inference, natural language processing).
• Develop models for sales forecasting, marketing mix optimization, customer segmentation (HCPs, payers, pharmacies), sales force effectiveness (SFE) analysis, incentive compensation modelling, and market access analytics (e.g., payer landscape, formulary impact).
• Analyze promotional effectiveness and patient persistency/adherence.
• Build models for patient journey mapping, patient segmentation for personalized interventions, treatment adherence prediction, disease progression modelling, and identifying drivers of patient outcomes from RWE.
• Contribute to understanding patient behavior, unmet needs, and the impact of interventions on patient health.

Generative AI & LLM Solutions:

• Extracting insights from unstructured text data (e.g., clinical notes, scientific literature, sales call transcripts, patient forum discussions).
• Summarization of complex medical or commercial documents.
• Automated content generation for internal use (e.g., draft reports, competitive intelligence summaries).
• Enhancing data augmentation or synthetic data generation for model training.
• Developing intelligent search or Q&A systems for commercial or medical inquiries.
• Apply techniques like prompt engineering, fine-tuning of LLMs, and retrieval-augmented generation (RAG).

Insight Generation & Storytelling:

• Transform complex analytical findings into clear, concise, and compelling narratives and actionable recommendations for both technical and non-technical audiences.
• Create impactful data visualizations, dashboards, and presentations using tools like Tableau, Power BI, or Python/R/Alteryx visualization libraries.

Collaboration & Project Lifecycle Management:

• Collaborate effectively with cross-functional teams including product managers, data engineers, software developers, and other data scientists.
• Support the entire data science lifecycle, from conceptualization and data acquisition to model development, deployment (MLOps), and ongoing monitoring in production environments.

Qualifications:

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Applied Mathematics, Economics, Bioinformatics, Epidemiology, or a related quantitative field.
• 4+ years progressive experience as a Data Scientist, with demonstrated success in applying advanced analytics to solve business problems, preferably within the healthcare, pharmaceutical, or life sciences industry using pharma dataset extensively (e.g. sales data from Iqvia, Symphony, Komodo, etc., CRM data from Veeva, OCE, etc.)
• Must-have: Solid understanding of pharmaceutical commercial operations (e.g., sales force effectiveness, marketing, market access, CRM).
• Must-have: Experience working with real-world patient data (e.g., claims, EHR, pharmacy data, patient registries) and understanding of patient journeys.
• Strong programming skills in Python (e.g., Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and/or R for data manipulation, statistical analysis, and machine learning.
•  Expertise in SQL for data extraction, manipulation, and analysis from relational databases.
• Experience with machine learning frameworks and libraries.
• Proficiency in data visualization tools (e.g., Tableau, Power BI) and/or visualization libraries (e.g., Matplotlib, Seaborn, Plotly).
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is a significant advantage.
• Specific experience with Natural Language Processing (NLP) techniques, Generative AI models (e.g., Transformers, diffusion models), Large Language Models (LLMs), and prompt engineering is highly desirable.
• Experience with fine-tuning LLMs, working with models from Hugging Face, or utilizing major LLM APIs (e.g., OpenAI, Anthropic, Google).
• Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).
• Knowledge of pharmaceutical or biotech industry regulations and compliance requirements like HIPAA, CCPA, SOC, etc.
• Excellent communication, presentation, and interpersonal skills, with the ability to effectively interact with both technical and non-technical stakeholders at all levels.
• Attention to details, biased for quality and client centricity.
• Ability to work independently and as part of a cross-functional team.
• Strong leadership, mentoring, and coaching skills.

Benefits:

• Competitive salary and benefits package.
• Opportunity to work on cutting-edge Analytics projects, transforming the life sciences industry
• Collaborative and supportive work environment.
• Opportunities for professional development and growth.