• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining. • Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems. • Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate. • Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance. • Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance. • Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems. • Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions. • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes. • Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration. • Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation. • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills. • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise. • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews). • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences. • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

• Design, build, train, evaluate, and deploy machine learning models. • Develop predictive models including churn, propensity, lead scoring, customer lifetime value, recommendation engines, forecasting, and marketing attribution. • Perform statistical analysis, hypothesis testing, causal inference, and A/B test analysis. • Build feature engineering and model training pipelines. • Deploy and monitor production ML models, including model drift detection and retraining. • Collaborate with Product, Engineering, Analytics, and executive leadership. • Mentor junior data scientists and establish best practices.

Role Description - Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. - Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). - Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. - Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. - Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). - Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. - Monitor model performance, conduct error analysis, and continuously optimize pipelines. Qualifications - 10+ years of experience in data science with deep hands‑on expertise in NLP and Generative AI. - Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK). - Strong Python skills with experience in PyTorch/TensorFlow for advanced model development. - Practical experience building RAG architectures, vector search, and prompt optimization. - Solid understanding of MLOps, model deployment, monitoring, and productionization. - Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.

Role Description - Lead and mentor a team of data scientists and analysts; provide technical guidance and ensure high-quality deliverables. - Design, develop, and optimize machine learning models (classification, regression, clustering, forecasting, etc.). - Build and maintain big data processing pipelines using PySpark, Spark SQL, and distributed computing environments. - Architect and deploy scalable ML solutions on Azure (Azure Databricks, Azure ML, ADLS, ADF). - Oversee feature engineering, model lifecycle management, monitoring, and performance tuning. - Collaborate with cross-functional teams to translate business needs into analytical solutions. - Present insights, model outputs, and recommendations to technical and business stakeholders. Qualifications - 10+ years of hands-on experience in data science and ML development. - Strong hands-on experience in leading complex analytical problem solving using traditional ML architecture (like supervised and unsupervised learning, time series and Deep learning) along with strong team leadership capabilities. - Expertise in Python, PySpark, scikit‑learn, XGBoost, and related ML libraries. - Strong experience with Azure data and ML services. - Solid understanding of distributed computing and performance optimization using Spark. - Proven ability to lead technical teams, conduct code reviews, mentor juniors, and manage project execution. - Excellent communication, stakeholder management, and problem‑solving skills. - Experience with MLOps practices and CI/CD pipelines.