Data Scientist

Data Scientist - Gen AI Applications Position Overview

Key Responsibilities

**Statistical Analysis & Machine Learning**
- Conduct statistical analysis and hypothesis testing to validate model performance and business impact
- Build and optimize traditional machine learning models (regression, classification, clustering, time series, etc.)
- Perform feature engineering, selection, and dimensionality reduction techniques
- Design and execute A/B tests and controlled experiments to measure model effectiveness
- Develop predictive models and recommendation systems to support business decision-making

**Data Analysis & Insights**
- Analyze large, complex datasets to extract actionable insights and identify patterns
- Create comprehensive data visualizations and dashboards to communicate findings to stakeholders
- Perform exploratory data analysis (EDA) to understand data distributions, correlations, and anomalies
- Conduct cohort analysis, funnel analysis, and other business intelligence techniques
- Develop KPIs and metrics frameworks to measure success of AI initiatives

**GenAI Model Development & Implementation**
- Design experiments to evaluate and compare different generative AI approaches
- Implement fine-tuning strategies for pre-trained models using statistical optimization techniques
- Develop RAG (Retrieval-Augmented Generation) systems with focus on data quality and retrieval accuracy

**Research & Innovation**
- Stay current with latest developments in generative AI, including emerging architectures and techniques
- Experiment with novel approaches to improve model performance, efficiency, and safety
- Evaluate and benchmark different model architectures and frameworks

**Cross-functional Collaboration**
- Partner with product managers to translate business requirements into technical solutions
- Work closely with MLOps engineers to ensure scalable deployment and monitoring
- Communicate complex technical concepts to stakeholders across various technical backgrounds

Required Qualifications

**Technical Skills**
- Bachelors, Master or Ph.D in Computer Science, Data Science, Statistics, Mathematics, or related field
- 6+ years of experience in data science with strong foundation in statistical methods and machine learning
- Expert proficiency in Python/R and data science libraries (pandas, numpy, scikit-learn, statsmodels, matplotlib, seaborn)
- Strong knowledge of statistical modeling, regression analysis, and experimental design
- Experience with SQL and database management for large-scale data analysis
- Proficiency in data visualization tools (Tableau, Power BI, or similar)
- Knowledge of machine learning frameworks (PyTorch, TensorFlow, XGBoost, LightGBM)
- Experience with cloud platforms (AWS, GCP, Azure) and distributed computing
- MLOps proficiency with tools like MLflow, Kubeflow, Docker, and CI/CD pipelines
- Familiarity with cloud ML platforms such as AWS SageMaker, Google Vertex AI, or Azure ML
- Strong understanding of data preprocessing, feature engineering, and model validation techniques

**GenAI & Advanced ML**
- Knowledge of deep learning architectures, particularly transformer models
- Experience with model evaluation metrics and statistical significance testing
- Understanding of bias detection, model interpretability, and responsible AI practices
- Familiarity with large language models and generative AI evaluation methodologies

**Soft Skills**
- Strong analytical and problem-solving abilities
- Excellent communication skills with ability to explain complex technical concepts
- Experience working in agile development environments
- Self-motivated with ability to work independently and in team settings

Preferred Qualifications
- Experience with advanced statistical methods (Bayesian analysis, causal inference, time series forecasting)
- Knowledge of optimization algorithms and mathematical programming
- Experience with multivariate testing and experimental design
- Background in natural language processing or computer vision from a data science perspective
- Experience with model compression and efficiency optimization techniques
- Experience leading data science projects and mentoring junior data scientists
- Data engineering experience including pipeline development, data preprocessing, and model deployment workflows

Pay: ₹568,752.26 - ₹3,519,554.93 per year

**Benefits**:

- Health insurance
- Life insurance
- Paid sick time
- Provident Fund
- Work from home

Schedule:

- Day shift
- Monday to Friday

Supplemental Pay:

- Performance bonus

Work Location: Remote

Application Deadline: 10/07/2025
Expected Start Date: 28/07/2025