Data Scientist

About Gradera — Digital Twin & Physical AI Platform 

At Gradera, we are building a next-generation Digital Twin and Physical AI platform that enables enterprises to model, simulate, and optimize complex real-world systems. Our work brings together strategy, architecture, data, simulation, and experience design to power decision-making across large-scale operational environments such as manufacturing, logistics, and supply chain networks. 

This platform-led initiative applies AI-native execution, advanced simulation, and governed orchestration to help organizations test scenarios, predict outcomes, and continuously improve performance. We operate with an enterprise-first mindset prioritizing reliability, transparency, and measurable business impact as we build intelligent systems that scale beyond a single industry or use case. 

Data Scientist 

Overview 

We are seeking a versatile Data Scientist to join our Simulation & Scenario Enablement team. This is a high-impact role combining advanced machine learning, statistical modeling, and exploratory data analysis to power digital twin simulations and scenario planning capabilities. You will transform complex operational data into actionable insights, build predictive models that drive simulation accuracy, and develop surrogate models that enable real-time what-if analysis. This role spans the full analytics spectrum — from deep-dive EDA and hypothesis testing to production ML models integrated with physics-based simulations. 

Our core data science stack includes: 

Machine Learning & MLOps 

• Databricks ML for end-to-end model development and deployment 

• MLflow for experiment tracking, model registry, and deployment 

• Feature Store for feature engineering and management 

• Unity Catalog for ML asset governance 

Analytics & Exploration 

• Databricks SQL and Notebooks for interactive analysis 

• PySpark for large-scale data processing 

• Python (pandas, NumPy, scikit-learn, SciPy) for statistical analysis 

• Visualization libraries (Matplotlib, Seaborn, Plotly) 

Advanced Modeling 

• Time-series forecasting (Prophet, statsmodels, neural forecasting) 

• Physics-informed machine learning approaches 

• Surrogate modeling for simulation acceleration 

• Optimization algorithms (OR-Tools, SciPy optimize) 
  
  

Key Responsibilities 

• Perform deep-dive exploratory data analysis on operational datasets using Databricks 

• Conduct statistical analysis, hypothesis testing, and data profiling for quality and fitness 

• Build dashboards and visualizations to communicate insights to stakeholders 

• Identify patterns, anomalies, and correlations to inform simulation and scenario design 

• Translate business questions into analytical frameworks and actionable insights 

• Build and deploy ML models for forecasting, classification, and regression 

• Develop surrogate and physics-informed ML models for real-time scenario evaluation 

• Design and manage features using Databricks Feature Store 

• Track experiments, version models, and deploy using MLflow 

• Integrate ML with simulations, validate via back-testing, and document model performance 
  

Preferred Qualifications 

• 8+ years of experience in data science, analytics, or quantitative research roles 

• Master’s or PhD in a quantitative field (Statistics, Applied Mathematics, Physics, Computer Science, Engineering, or related) 

• Track record of delivering ML models and analytical insights in production environments 

• Experience working with large-scale data on distributed platforms (Spark, Databricks) 

• Experience in cross-functional teams with data engineers, ML engineers, and domain experts 
  
  

Highly Desirable 

• Experience with data science for digital twin or simulation platforms 

• Background in operations research, industrial engineering, or computational physics 

• Experience building surrogate models for complex physical or operational systems 

• Familiarity with discrete event simulation (SimPy, AnyLogic) or agent-based modeling 

• Experience with Bayesian methods, probabilistic programming, or uncertainty quantification 

• Publications or patents in applied ML, forecasting, or simulation 

• Exposure to industrial domains such as Manufacturing, Logistics, or Transportation is a plus 

Location: Hyderabad, Telangana 
Department: Engineering 
Employment Type: Full-Time