Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level ML role, Bangalore metro, common title and experience range increases candidate competition.
Time-series forecasting focus and retail preference moderately reduce cross-industry transferability.
Explicit 4–6 years plus mandatory time-series, AWS Sagemaker, ML frameworks, and MLOps skills increases filtering strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Lead data preparation, profiling, and end-to-end development of predictive models for demand planning and forecasting using time-series techniques.
Build, evaluate, and deploy forecasting models (ARIMA, LightGBM, Prophet, LSTMs/Transformers) for sequential data in retail domain context.
Travel to client office in Bengaluru twice a week to collaborate and present technical work from both technical and business perspectives.
Minimum Requirements
4–6 years of hands-on experience in Machine Learning / Data Science focusing on time-series forecasting.
Strong expertise in time-series forecasting methods, data preparation, and exploratory data analysis with large-scale datasets.
Advanced proficiency in Python and SQL; experience with AWS (Sagemaker), ML frameworks (pandas, scikit-learn, PyTorch/TensorFlow, statsmodels, Prophet), and MLOps tools (Docker, Airflow, MLflow).
Must be available to travel to Bengaluru client site twice a week.
Ideal Candidate Profile
Experienced working independently owning machine learning projects end-to-end from data to deployment with minimal supervision.
Has retail domain experience, particularly in demand planning, SKU-level forecasting, or stockout prediction.
Able to communicate complex technical details effectively to business stakeholders and customers.