About The Role

We are seeking a proactive, eager-to-learn professional to work within a team of experienced data scientists and modelers. You will get hands-on exposure to the entire lifecycle of analytical forecasting projects — from data exploration to model deployment, validation, and testing — while receiving mentorship and training to build your technical and business skills.

Key Responsibilities

Demand Forecasting & Modeling Support: Assist in developing and maintaining short-term and long-term demand forecasts at the Automotive Segmentation and Powertrain levels. Support the building and validation of forecasting models (time-series models, AI/ML algorithms) to identify market trends, seasonal patterns, and macroeconomic influences, under guidance from senior team members.
Business Engagement and Presentation: Support Sales, Marketing, and Finance teams by helping translate analytical outputs into clear summaries. Assist in preparing monthly forecasting reviews and contribute to presentations for stakeholders.
Visualization & Reporting: Help design and maintain dashboards that provide leadership with visibility into sales performance and forecast accuracy.
Process Innovation using AI: Learn to integrate Generative AI and other modern AI tools into daily workflows to help automate repetitive tasks and accelerate data insights.
Cross-Functional Collaboration: Work with business and engineering teams to understand data requirements, and practice translating technical findings into clear, data-driven summaries for stakeholders.

Technical Skills & Qualifications

Forecasting & Statistics: Solid academic foundation in statistical theory (e.g., hypothesis testing, regression analysis) and exposure to time-series methodologies (e.g., ARIMA, Prophet, etc.) through coursework or projects.
AI/ML Fundamentals: Academic or project experience with machine learning concepts (Supervised/Unsupervised learning) applied to predictive analytics problems.
Programming: Working knowledge of Python (Pandas, NumPy, Scikit-learn) or R for data manipulation and modeling. Familiarity with SQL for data extraction is a plus.
Data Visualization: Interest in Data Visualization tools - Power BI, Tableau, or similar tools through coursework, internships, or personal projects.
AI Tool Integration: Interest and basic experience in using AI productivity tools (e.g., LLMs, GitHub Copilot) for coding assistance and workflow optimization.

Relevant Coursework (Preferred/Expected for Freshers)

Probability Theory & Statistical Inference, Statistical Mathematics – probability distributions, hypothesis testing, confidence intervals
Applied Regression Analysis – linear, logistic, and multivariate regression
Time Series Modeling – ARIMA, exponential smoothing, seasonal decomposition, forecasting techniques
Machine Learning / Predictive Analytics – supervised and unsupervised learning algorithms (decision trees, clustering, random forests, etc.)
Programming for Data Science – Labs on either with Python or R (Pandas, NumPy, Scikit-learn, ggplot2)
Database Management & SQL – querying, joins, data extraction and manipulation
Data Visualization & Business Intelligence – Power BI, Tableau, or Matplotlib/Seaborn
Applied Econometrics – Macroeconomic and demand forecasting contexts
Data Mining / Big Data Fundamentals – exposure to large-scale data handling concepts
Linear Algebra & Multivariate Calculus – foundational math for statistical modeling and ML
Academic Project in Forecasting or Predictive Modeling – hands-on project applying statistical/ML techniques to a real or simulated dataset

Behavioral Competencies

Curious & Innovative: A natural tendency to ask "why" and a drive to investigate the root causes of data anomalies.
Quick Learner: Eagerness to rapidly pick up new technical tools and adapt to evolving automotive market dynamics (e.g., EV transition, software-defined vehicles).
Team Player: Collaborative mindset with the ability to work effectively across cross-functional and global teams.
Strong Communication and Presentation Skills
Business Acumen: A keen interest in the automotive industry and a willingness to learn how data patterns connect to real-world business outcomes.
Master’s degree in Statistics, Econometrics (Mandatory)
0–1 years of experience — internships, academic projects, or coursework in data analytics, forecasting, or statistical modeling are highly valued (Automotive-related projects are a plus).