
Head of Modelling & Data Science
- Coimbatore, Tamil Nadu
- Permanent
- Full-time
- Develop, implement, and maintain advanced analytical models using machine learning algorithms and GenAI applications
- Utilize various advanced analytics techniques to uncover trends, patterns, and insights from large and complex datasets.
- Collaborate with cross-functional teams to identify business needs and deliver data-driven solutions.
- Create visually compelling dashboards and reports to present findings to stakeholders.
- Continuously evaluate and improve existing analytics methodologies and models to enhance accuracy and performance.
- Stay abreast of industry trends and advancements in analytics and machine learning to drive innovation within the team.
- Mentor junior team members and contribute to knowledge sharing within the organization.
- Bachelor’s or Master’s degree in Data Science, Business Analytics, Mathematics, Statistics, or a related field.
- 10+ years of experience in advanced analytics, data science, machine learning, Generative AI or a related field.
- Strong experience with quantitative modeling, predictive analytics, text analytics, and forecasting methodologies
- Proficiency in SQL (or Google BigQuery), Python, visualization tools like Tableau/PowerBI
- Familiarity with the Retail/CPG/Tech industry and experience with product, transaction, and customer-level data.
- Excellent communication skills, both verbal and written, with the ability to convey complex concepts to non-technical stakeholders.
- Strong analytical and problem-solving skills, with an inquisitive mindset.
- Descriptive Analytics: Statistical analysis, data visualization.
- Predictive Analytics: Regression analysis, time series forecasting, classification techniques, market mix modeling
- Prescriptive Analytics: Optimization, simulation modeling.
- Text Analytics: Natural Language Processing (NLP), sentiment analysis.
- Supervised Learning: Linear regression, logistic regression, decision trees, support vector machines, random forests, gradient boosting machines among others
- Unsupervised Learning: K-means clustering, hierarchical clustering, principal component analysis (PCA), anomaly detection among others
- Reinforcement Learning: Q-learning, deep Q-networks, etc.
- Researching, loading and application of the best LLMs (GPT, Gemini, LLAMA, etc.) for various objectives
- Hyper parameter tuning
- Prompt Engineering
- Embedding & Vectorization
- Fine tuning