Job Description: Senior Data Scientist (Statistics, ML & Generative AI)

Position Overview

Key Responsibilities

• Core Statistical & ML Modeling:
  
  
  • Apply advanced probability theory, hypothesis testing, Bayesian methods, and statistical inference to validate data distributions, perform predictive analytics, and design robust experiments (A/B testing).
  • Build, fine-tune, and evaluate classical machine learning algorithms (regression, classification, clustering, time-series forecasting, ensemble methods) for production systems.
• Generative AI & LLM Engineering:
  
  
  • Design and implement Generative AI architectures, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent frameworks, and vector search systems.
  • Fine-tune, evaluate, and prompt-engineer open-source and proprietary Large Language Models (LLMs) for specific domain applications.
  • Implement guardrails, evaluation frameworks (e.g., RAGAS, DeepEval), and toxicity/bias detection for LLM applications.
• Data Engineering & System Architecture:
  
  
  • Collaborate with engineering teams to deploy ML/LLM models into production microservices via REST APIs, gRPC, or async worker queues.
  • Optimize model inference latency, throughput, and GPU/memory consumption.
  • Design data pipelines using Python, SQL, and modern data orchestration toolkits.
• Thought Leadership & Collaboration:
  
  
  • Partner with product and business stakeholders to translate complex business problems into scalable machine learning and AI frameworks.
  • Stay at the forefront of emerging research in machine learning, deep learning, and generative AI.

Required Qualifications & Technical Skills

• Education: Bachelor's, Master's, or Ph.D. in Computer Science, Statistics, Applied Mathematics, Data Science, Quantitative Finance, or a related quantitative field.
• Core Mathematics & Statistics:
  
  
  • Strong expertise in probability distributions, stochastic processes, decision theory, regression analysis, and variance reduction techniques.
• Classical ML & Deep Learning:
  
  
  • Proficiency with ML frameworks (scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow).
  • Deep understanding of loss functions, optimization algorithms, evaluation metrics (ROC-AUC, RMSE, Precision/Recall, BLEU, ROUGE), and cross-validation techniques.
• Generative AI & LLM Ecosystem:
  
  
  • Practical experience with LLM frameworks like LangChain, LangGraph, LlamaIndex, or DSPy.
  • Hands-on experience with Vector Databases (ChromaDB, Pinecone, pgvector, Qdrant, or FAISS).
  • Knowledge of quantization techniques (LoRA, QLoRA), embedding models, and multi-agent coordination frameworks (e.g., Model Context Protocol / MCP servers).
• Programming & Tools:
  
  
  • High proficiency in Python (Pandas, NumPy, SciPy, PyTorch) and complex SQL.
  • Familiarity with containerization (Docker) and modern development workflows (Git, CI/CD).

Preferred Qualifications

• Experience with time-series forecasting, quantitative modeling, or risk prediction algorithms.
• Knowledge of MLOps practices, model monitoring, and pipeline orchestration tools.
• Familiarity with cloud platforms (AWS, GCP, or Azure).