Senior Data Scientist Full-time

Senior Data Scientist (Marketing Mix/ ML)

At Roche, you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted, and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop, and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

Roche has established the Global Analytics and Technology Center of Excellence (GATE) to drive analytics- and technology-driven solutions by partnering with Roche affiliates across the globe. GATE enables data-led decision-making and innovation across healthcare and biotech operations.

As a Senior Data Scientist, you will lead and deliver high-impact data science projects that enable strategic decision-making within Roche's healthcare and commercial ecosystem. Collaborating closely with cross-functional teams, your primary focus will be on Healthcare and Pharma Data Science and Marketing Mix Modeling (MMM) leveraging advanced statistical and machine-learning techniques to generate actionable insights and optimize business outcomes.

Exposure to GenAI, LLM, NLP, and ML Ops will be considered an added advantage, supporting Roche's future-ready analytics and AI roadmap.

Your Opportunity:

Advanced Analytics (Primary Focus):

• Collect, clean, and prepare large and complex healthcare-related datasets (product performance, patient data, operational metrics, etc.) for analysis
• Develop and implement statistical and machine learning models (e.g., multivariate regression, time-series analysis, XGBoost, clustering, classification, causal inference) to address complex business problems and uncover meaningful insights
• Utilize advanced data analytics techniques to explore and identify patterns, trends, and root causes, applying methodologies such as clustering, classification, and causal inference

Marketing and Experimental Analytics (Primary Focus):

• Build Econometric/market mix models (MMM), multi-touch attribution models (MTA), optimize marketing spend and come-up with implementable recommendations and strategy/plan
• Lead the development and implementation of advanced Media Mix Models to inform and optimize marketing spend across multiple channels (e.g., TV, digital, print, radio)
• Design and execute complex statistical analyses to evaluate the effectiveness of marketing strategies and optimize resource allocation
• Apply experimental design and A/B testing methodologies to validate and measure marketing and operational initiatives

Project Leadership and Collaboration (Primary Focus):

• Lead and manage end-to-end data science projects from problem definition to model deployment ensuring alignment with business goals and timelines
• Partner with cross-functional teams to define business requirements and deliver tailored analytics solutions
• Develop and maintain key stakeholder relationships to ensure effective communication and collaboration throughout project lifecycles
• Present analytical findings and strategic recommendations to business stakeholders, influencing data-driven decision making across multiple levels of the organization

Communication, Mentorship, and Governance (Primary Focus):

• Translate complex data insights into clear and actionable business strategies that address stakeholder needs and expectations
• Mentor and guide junior data scientists, providing technical expertise and fostering an environment of continuous learning and improvement
• Promote best practices in coding, data handling, and project management within the data science team, ensuring high-quality deliverables
• Ensure adherence to Roche's ethical AI standards and data privacy regulations.

GenAI, NLP, and Machine Learning Operations (Emerging Focus):

• Develop and implement GenAI models and tools to solve business problems
• Deploy machine learning models in production environments, ensuring robust ML Ops practices for model monitoring, maintenance, and scaling
• Collaborate with IT and DevOps teams to streamline the integration of ML models into existing systems and workflows
• Enhance agent performance through experimentation with LLMs, prompt tuning, and advanced reasoning workflows

Who you are:

• You hold a bachelor's degree in Technology or a relevant discipline, with a preference for Computer Science, Software, Statistics, Data Science, AI, Machine Learning, Data Engineering and related fields. Preferably, you have a Master's degree
• Certifications in AI/ML, Data Science, or related technologies would be a plus
• You have 5-8 years of hands-on experience in data science, with proven experience in leading data science projects within the pharma/biotech/healthcare domain
• Strong proficiency in Python and SQL, with experience in data wrangling, feature engineering, and analytical model development
• Experience working in cloud-based environments (AWS preferred), with practical knowledge of GitHub and cloud computing workflows for data science projects
• Hands-on experience building models using algorithms and techniques such as multivariate regression, time series analysis, XGBoost, clustering, classification, OLS regression, Naïve Bayes, linear and time-decay attribution models, Markov chains, and Shapley value methods
• Experience in Multi channel Marketing Mix Modeling (MMM), or related fields, with a track record of delivering impactful results
• At least 4 years of strong experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, Keras, spaCy, NLTK, NumPy, pandas, and Spark
• Basic understanding of pharmaceutical datasets (e.g., IQVIA, SHA, Patients data) and familiarity with US healthcare markets would be a plus
• Strong analytical and problem-solving skills with a data-driven mindset

Good to Have:

• Experience with modern deep learning architectures for NLP, including hands-on experience with transformers (e. g., Hugging Face, SBert, GPT-2/GPT-3) and the capability to build ML/DL pipelines for training/tuning models, including transfer learning
• Proficiency with LLM APIs (e. g., OpenAI, Claude, Gemini) and agent frameworks such as AutoGen, LangGraph, AgentBuilder, or CrewAI
• Experience with prompt engineering or intelligent workflow automation
• Bash/shell scripting is a plus, Experience with Docker, API development is advantageous

Note: This job description is intended as a general guideline for the responsibilities and qualifications required for this position. It is not an exhaustive list, and responsibilities may evolve and change based on business needs.