Data Scientist Jobs in Hyderabad

About This Role

InnoSoft India is hiring a Data Scientist for its Hyderabad AI team. The company is embedding machine learning capabilities into its ERP platform for manufacturing clients, and this role focuses on building predictive models for demand forecasting, equipment maintenance scheduling, and inventory optimization. You will work on problems that have immediate operational impact — models are deployed into live client environments, not just evaluated internally.

Role Context

Reporting to the Data Science Lead, you will work within a three-person data science team alongside ML engineers and a data engineer who manages the feature pipelines. The team owns the full model lifecycle from problem framing and data exploration through deployment and monitoring. Client domain context matters here — models for manufacturing inventory behave differently from e-commerce demand models, and you will be expected to develop this domain understanding as part of the role.

Key Responsibilities

• Develop demand forecasting models for manufacturing inventory using time-series methods and regression
• Build predictive maintenance models using sensor data and historical equipment failure records
• Perform exploratory data analysis and feature engineering for model development and improvement cycles
• Evaluate model performance rigorously using appropriate metrics and validate on held-out client datasets
• Deploy models into production using Flask APIs integrated with the InnoSoft ERP platform
• Monitor model performance in production and retrain on updated data when drift is detected
• Communicate model behavior, assumptions, and limitations clearly to product and client teams
• Collaborate with data engineers to define feature requirements for the production feature store

Requirements & Skills

• 2–4 years of data science or ML engineering experience with Python as the primary tool
• Proficiency in scikit-learn, XGBoost, and time-series libraries such as statsmodels or Prophet
• Strong statistical foundation covering regression, classification, and model evaluation methodology
• Experience deploying ML models into production via REST API endpoints using Flask or FastAPI
• Solid SQL skills for feature extraction and exploratory analysis on structured manufacturing datasets
• Familiarity with model monitoring concepts including data drift detection and performance degradation
• Manufacturing domain knowledge or prior work with industrial operations data is an advantage

Benefits & Work Conditions

• Monthly salary of ₹40,000–₹80,000 commensurate with ML experience and domain knowledge depth
• Access to GPU compute resources for model training on larger datasets
• Annual budget of ₹20,000 for ML conferences, courses, or certifications
• PF, ESI, and full statutory benefits from the joining date
• Direct access to client operational data and visible deployment impact from early in the role

Who Should Apply

This role fits data scientists who are done building models that stay in Jupyter notebooks. If you want to see your demand forecasting model actually reduce a client's overstock losses, enjoy the challenge of manufacturing domain data, and are comfortable owning the full model lifecycle from ideation to production monitoring, InnoSoft India's Hyderabad AI team offers that end-to-end experience with real manufacturing clients.

About the Company

InnoSoft India is a Hyderabad-based ERP software company that has been integrating machine learning capabilities into manufacturing and supply chain operations since 2020. The AI team works directly with production data from over 150 manufacturing clients, applying predictive analytics to real operational challenges. The company provides strong data infrastructure support and prioritizes model quality and deployment reliability over rapid experimentation volume.

How to Apply

Apply through this portal with your resume and a brief description of one ML model you built and deployed. Include the problem, your approach, and how you measured success. Shortlisted candidates will be contacted within 5 working days for a technical discussion.