Marketing Data Scientist

Experience : 8+ Years Mandatory skills: Python, Gen AI, traditional ML, core Data scientist, ML Ops, Agentic AI

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.
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
Conduct statistical inference, confidence intervals, and significance testing
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
Stay current with latest AI/ML trends and technologies
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
~ Hypothesis testing, regression analysis, ANOVA, time series analysis, experimental design, Bayesian inference
~ Statistical Tools: Experience with R, SAS, SPSS, or similar statistical software packages
~ 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
~ Experience with agile development methodologies
~ Preferred Qualifications Experience with causal inference methods and econometric techniques
~ 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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