Data Scientist

Experience : 8+ Years

Mandatory skills: Python, Gen AI, traditional ML, core Data scientist, ML Ops, Agentic AI

Position Overview

We are seeking an experienced AI Architect to join our dynamic team. This role combines deep technical expertise in traditional statistics, classical machine learning, and modern AI with full-stack development capabilities to build end-to-end intelligent systems. You'll work on revolutionary projects involving generative AI, large language models, and advanced data science applications.

Key Responsibilities

- AI/ML Development & Data Science:Design, develop, and deploy machine learning models ranging from classical algorithms to deep learning for production environments
- Apply traditional statistical methods including hypothesis testing, regression analysis, time series forecasting, and experimental design
- Build and optimize large language model applications including fine-tuning, prompt engineering, and model evaluation
- Implement Retrieval Augmented Generation (RAG) systems for enhanced AI capabilities
- Conduct advanced data analysis, statistical modeling, A/B testing, and predictive analytics using both classical and modern techniques
- Research and prototype cutting-edge generative AI solutions
- Traditional ML & Statistics:Implement classical machine learning algorithms including linear/logistic regression, decision trees, random forests, SVM, clustering, and ensemble methods
- Perform feature engineering, selection, and dimensionality reduction techniques
- Conduct statistical inference, confidence intervals, and significance testing
- Design and analyze controlled experiments and observational studies
- Apply Bayesian methods and probabilistic modeling approaches
- Full Stack Development:Develop scalable front-end applications using modern frameworks (React, Vue.js, Angular)
- Build robust backend services and APIs using Python, Node.js, or similar technologies
- Design and implement database solutions (SQL/NoSQL) optimized for ML workloads
- Create intuitive user interfaces for AI-powered applications and statistical dashboards
- MLOps & Infrastructure:Establish and maintain ML pipelines for model training, validation, and deployment
- Implement CI/CD workflows for ML models using tools like MLflow, Kubeflow, or similar
- Monitor model performance, drift detection, and automated retraining systems
- Deploy and scale ML solutions using cloud platforms (AWS, GCP, Azure)
- Containerize applications using Docker and orchestrate with Kubernetes
- Collaboration & Leadership:Work closely with data scientists, product managers, and engineering teams
- Mentor junior engineers and contribute to technical decision-making
- Participate in code reviews and maintain high development standards
- Stay current with latest AI/ML trends and technologies

Required Qualifications

- Experience & Education:7-8 years of professional software development experience
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Machine Learning, Data Science, or related field
- 6+ years of hands-on AI/ML experience in production environments
- Technical Skills:Programming: Expert proficiency in Python, strong experience with JavaScript/TypeScript, R is a plus
- Traditional ML: Scikit-learn, XGBoost, LightGBM, classical algorithms and ensemble methods
- Statistics: Hypothesis testing, regression analysis, ANOVA, time series analysis, experimental design, Bayesian inference
- Statistical Tools: Experience with R, SAS, SPSS, or similar statistical software packages
- Deep Learning: TensorFlow, PyTorch, neural networks, computer vision, NLP
- LLM Experience: Working with GPT, Claude, Llama, or similar models; experience with fine-tuning and prompt engineering
- RAG Implementation: Vector databases (Pinecone, Weaviate, Chroma), embedding models, semantic search
- Data Science: Pandas, NumPy, statistical analysis, data visualization (Matplotlib, Plotly, Seaborn), feature engineering
- Full Stack: React/Vue.js, Node.js/FastAPI, REST/GraphQL APIs
- Databases: PostgreSQL, MongoDB, Redis, vector databases
- MLOps: Docker, Kubernetes, CI/CD, model versioning, monitoring tools
- Cloud Platforms: AWS/GCP/Azure, serverless architectures
- Soft Skills:Strong problem-solving and analytical thinking
- Excellent communication and collaboration abilities
- Self-motivated with ability to work in fast-paced environments
- Experience with agile development methodologies
- Preferred QualificationsExperience with causal inference methods and econometric techniques
- Knowledge of distributed computing frameworks (Spark, Dask)
- Experience with edge AI and model optimization techniques
- Publications in AI/ML/Statistics conferences or journals
- Open source contributions to ML/statistical projects
- Experience with advanced statistical modeling and multivariate analysis
- Familiarity with operations research and optimization techniques

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