Data Scientist

Experience : 8+ YearsMandatory skills: Python, Gen AI, traditional ML, core Data scientist, ML Ops, Agentic AIPosition OverviewWe 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 ResponsibilitiesAI/ML Development & Data Science: Design, develop, and deploy machine learning models ranging from classical algorithms to deep learning for production environmentsApply traditional statistical methods including hypothesis testing, regression analysis, time series forecasting, and experimental designBuild and optimize large language model applications including fine-tuning, prompt engineering, and model evaluationImplement Retrieval Augmented Generation (RAG) systems for enhanced AI capabilitiesConduct advanced data analysis, statistical modeling, A/B testing, and predictive analytics using both classical and modern techniquesResearch and prototype cutting-edge generative AI solutionsTraditional ML & Statistics: Implement classical machine learning algorithms including linear/logistic regression, decision trees, random forests, SVM, clustering, and ensemble methodsPerform feature engineering, selection, and dimensionality reduction techniquesConduct statistical inference, confidence intervals, and significance testingDesign and analyze controlled experiments and observational studiesApply Bayesian methods and probabilistic modeling approachesFull 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 technologiesDesign and implement database solutions (SQL/NoSQL) optimized for ML workloadsCreate intuitive user interfaces for AI-powered applications and statistical dashboardsMLOps & Infrastructure: Establish and maintain ML pipelines for model training, validation, and deploymentImplement CI/CD workflows for ML models using tools like MLflow, Kubeflow, or similarMonitor model performance, drift detection, and automated retraining systemsDeploy and scale ML solutions using cloud platforms (AWS, GCP, Azure)Containerize applications using Docker and orchestrate with KubernetesCollaboration & Leadership: Work closely with data scientists, product managers, and engineering teamsMentor junior engineers and contribute to technical decision-makingParticipate in code reviews and maintain high development standardsStay current with latest AI/ML trends and technologiesRequired QualificationsExperience & Education: 7-8 years of professional software development experienceBachelor's or Master's degree in Computer Science, Statistics, Mathematics, Machine Learning, Data Science, or related field6+ years of hands-on AI/ML experience in production environmentsTechnical Skills:Programming: Expert proficiency in Python, strong experience with JavaScript/TypeScript, R is a plusTraditional ML: Scikit-learn, XGBoost, LightGBM, classical algorithms and ensemble methodsStatistics: Hypothesis testing, regression analysis, ANOVA, time series analysis, experimental design, Bayesian inferenceStatistical Tools: Experience with R, SAS, SPSS, or similar statistical software packagesDeep Learning: TensorFlow, PyTorch, neural networks, computer vision, NLPLLM Experience: Working with GPT, Claude, Llama, or similar models; experience with fine-tuning and prompt engineeringRAG Implementation: Vector databases (Pinecone, Weaviate, Chroma), embedding models, semantic searchData Science: Pandas, NumPy, statistical analysis, data visualization (Matplotlib, Plotly, Seaborn), feature engineeringFull Stack: React/Vue.js, Node.js/FastAPI, REST/GraphQL APIsDatabases: PostgreSQL, MongoDB, Redis, vector databasesMLOps: Docker, Kubernetes, CI/CD, model versioning, monitoring toolsCloud Platforms: AWS/GCP/Azure, serverless architecturesSoft Skills: Strong problem-solving and analytical thinkingExcellent communication and collaboration abilitiesSelf-motivated with ability to work in fast-paced environmentsExperience with agile development methodologiesPreferred Qualifications Experience with causal inference methods and econometric techniquesKnowledge of distributed computing frameworks (Spark, Dask)Experience with edge AI and model optimization techniquesPublications in AI/ML/Statistics conferences or journalsOpen source contributions to ML/statistical projectsExperience with advanced statistical modeling and multivariate analysisFamiliarity with operations research and optimization techniques