Lead Data Scientist

1 day ago


Bangalore, India C5i Full time

Job Summary: As a part of the data science team of C5i, we are looking for an experienced and strategic Senior Data Scientist to join our high-impact analytics team in the. This position will require you to work on the development of Resource Allocation Model that directly influences how we allocate marketing & media budgets, drive consumer demand, and enhance company’s performance. This is a hands-on technical role requiring deep expertise in data science, machine learning, and business acumen to solve complex problems in a fast-paced, consumer-centric environment. Job Responsibilities: Develop, validate, and scale Resource Allocation Model to quantify the impact of Sales & Marketing packages on sales and brand performance. Implement optimization algorithms to inform budget allocation and maximize marketing ROI across geographies and product portfolios. Lead the development of predictive and prescriptive models to support commercial, trade, and brand teams. Leverage PySpark to manage and transform large-scale retail, media, and consumer datasets. Build and deploy ML models using Python and TensorFlow , ensuring robust model performance and business relevance. Collaborate with marketing, category, and commercial stakeholders to embed insights into strategic decisions. Use GitHub Actions for version control, CI/CD workflows , DVC for data versioning , and reproducible ML pipelines. Present findings through compelling data storytelling and dashboards for senior leadership. Mentor junior data scientists and contribute to a culture of innovation and excellence. Requirements & Qualifications: 5+ years of hands-on experience in data science, preferably within the FMCG or retail domain. Proven track record of building and deploying Marketing Mix Models and/or media attribution models. Deep knowledge of optimization techniques (e.G., linear programming, genetic algorithms, constrained optimization). Advanced programming skills in Python (pandas, scikit-learn, statsmodels, TensorFlow). Expertise in PySpark for distributed data processing and transformation. Experience with Git and GitHub Actions for collaborative development and CI/CD pipelines. Strong grounding in statistics, experimental design (A/B testing), and causal inference.


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