Lead Data Scientist
- Posted 8 days ago
- Be among the first 10 applicants
Job Description
Required Skills:
- 8–12 years of experience in data science or related analytics and statistical modelling roles.
- Own and drive endtoend AI/ML solutions—from problem definition and experimentation to production deployment and continuous optimization
- Define and lead technical strategy, including model selection, evaluation metrics, and tradeoffs across performance, scalability, and constraints
- Design and deliver advanced AI solutions (including NLP, LLMs, GenAI, and RAG) aligned with business outcomes
- Act as the primary technical authority, guiding data scientists and ML engineers while proactively identifying and resolving risks
- Establish and sign off on success criteria, including model evaluation metrics, quality benchmarks, and production readiness standards
- Ensure Responsible AI compliance, including governance, explainability, documentation, and audit readiness
- Collaborate with engineering teams to enable secure, scalable, and reliable production deployments using MLOps and CI/CD best practices
- Lead and mentor senior data scientists, driving fullstack data science delivery and fostering strong problemsolving and algorithmic thinking
- Apply deep technical expertise in Python, ML/DL frameworks (TensorFlow, PyTorch, Scikitlearn), and largescale data processing
- Leverage strong foundations in statistics, data engineering, and cloud platforms (Azure/AWS) for production-grade solutions
- Translate business requirements into technical solutions and influence crossfunctional stakeholders to drive strategic outcomes
Preferred
- Experience working within large investment banking or financial services organizations.
- Experience defining enterprise AI governance, ethics guidelines, risk frameworks, or regulatory compliance programs.
- Published research, patents, or conference presentations in AI, ML, NLP, or related fields.
- Experience leading multiteam or matrixed delivery organizations across regions and time zones.
- Industry reputation as a technical expert or AI thought leader.
Education:
- Master's degree in Statistics, Mathematics, or Computer Science with a Data Science certification, or an Engineering degree specializing in Data Science and Artificial Intelligence.
- Proficiency in programming languages such as Python, R, and SQL
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About Company
Skills:
probability , triton , Jax, Tensorflow, Pytorch, Docker, Ml, Azure ML, MLops, Kubernetes, TD3, DL, TensorRT, CNNs, ONNX, ppo, Optimization, linear algebra, CQL, Transformers, Ai, sequence models, SAC, RL, CI-CD
Skills:
Logistic Regression, Decision Tree, Linear Regression, Svm, scrapy, Machine Learning Algorithms, Sql, Random Forest, Pca, Nosql, Neural Network, Pandas, Gcp, Numpy, Spark, Azure, Python, AWS, scikit-learn, K-mean Clustering, R, scapy, BERT
Skills:
Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Hypothesis Testing, Machine Learning, Performance evaluation, Generative AI, Data Analysis, Model monitoring, Autonomous workflows, Feature engineering, Responsible AI principles, AI guardrails, Statistical Modeling, Experimental Design, Model explainability, AI agents, Multi-agent systems
Skills:
statistical data analysis , Sql, Clustering, Natural Language Processing, Jira, Machine Learning, Gitlab, Python, feature engineering, UNIX command line, data analysis libraries, Classification, Agile development frameworks, Azure AI Search, anomaly detection, ML Ops
Skills:
data engineering , Sql, Deep Learning, Rest Apis, MLops, Gcp, Data Architecture, Machine Learning, AWS, Kubernetes, Python, Azure, Docker, AI Security, LLMs, LlamaIndex, LLMOps, CI CD, Weaviate, RAG AI Agents, LangChain, CrewAI, Pinecone, LangGraph, Milvus, Governance, Vector Databases, Chroma, Generative AI