Senior Machine Learning & Data Scientist

We're looking for a hands-on Senior Machine Learning & Data Scientist to solve complex enterprise problems with statistics, machine learning, and Generative AI. Working with AI Platform Engineering, Product, and Solutions teams, you'll build production-ready models and AI capabilities that turn enterprise data into trusted predictions, recommendations, insights, and decision intelligence. You'll own the full ML lifecycle, from problem formulation and experimentation to training, evaluation, deployment readiness, monitoring, and continuous improvement.

Success is measured by model quality, explainability, business impact, robustness, and customer outcomes.

• Design, build, train, and evaluate production-grade machine learning, statistical, deep learning, and Generative AI models for enterprise decision-making.
• Design and curate high-quality datasets for model training, fine-tuning, evaluation, and continuous learning while ensuring data quality and governance.
• Develop NLP and LLM-assisted solutions for semantic understanding, entity resolution, document intelligence, information extraction, classification, summarization, and enterprise knowledge discovery.
• Design feature engineering pipelines, experimentation frameworks, confidence scoring, uncertainty estimation, and model evaluation methodologies.
• Fine-tune, adapt, and optimize Large Language Models (LLMs) using techniques such as Supervised Fine-Tuning (SFT), LoRA, QLoRA, instruction tuning, preference optimization, domain adaptation, and synthetic data generation.
• Build evaluation frameworks for machine learning and LLMs covering accuracy, groundedness, hallucination detection, factual consistency, explainability, bias, safety, and business outcome measurement.
• Collaborate closely with AI Platform Engineers to integrate trained models into production systems and continuously improve model performance.
• Partner with Product, Solutions, and customer teams to translate business problems into scalable AI and machine learning capabilities.
• Stay current with advances in machine learning, NLP, LLM training, and Decision Intelligence, applying new techniques where they deliver measurable customer value.

Required Qualifications

• 10+ years of hands-on experience in Data Science, Applied Machine Learning, or a related quantitative field.
• Strong expertise in statistical modeling, machine learning, feature engineering, experimentation, model evaluation, and solving real-world business problems.
• Proven experience designing, training, validating, and improving production machine learning or deep learning models.
• Hands-on experience training, fine-tuning, adapting, and evaluating Large Language Models (LLMs) using Supervised Fine-Tuning (SFT), LoRA, QLoRA, instruction tuning, preference optimization (DPO/RLHF concepts), model distillation, or domain adaptation.
• Experience building high-quality training datasets, evaluation datasets, synthetic datasets, labeling workflows, and feedback-driven model improvement pipelines.
• Strong understanding of transformer architectures, embeddings, tokenization, inference, context windows, model selection, and LLM evaluation techniques.
• Experience evaluating AI systems using benchmark datasets, human evaluation, statistical validation, confidence scoring, groundedness, hallucination detection, and business outcome metrics.
• Strong understanding of supervised and unsupervised learning, predictive modeling, recommendation systems, anomaly detection, forecasting, ranking, optimization, and time-series analysis.
• Strong Python skills with experience using Scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, or similar machine learning frameworks.
• Strong SQL skills and experience working with large, heterogeneous enterprise datasets across CRM, ERP, operational, financial, document, and SaaS systems.
• Strong understanding of data quality, explainability, uncertainty quantification, feature drift, concept drift, model monitoring, and production machine learning best practices.
• Excellent analytical, communication, and problem-solving skills with the ability to translate technical findings into actionable business insights.
• Ability to thrive in a fast-moving startup environment with strong ownership, curiosity, and a bias toward execution.

Nice to Have

• Master's or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research, Engineering, or a related quantitative discipline.
• Experience with vector databases, semantic retrieval, reranking, hybrid search, and embedding optimization.
• Experience working with Hugging Face Transformers, PEFT, TRL, Weights & Biases, MLflow, or similar ML tooling.
• Experience with cloud ML platforms such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
• Experience working in regulated, customer-controlled, VPC, on-premises, or privacy-sensitive AI environments.
• Contributions to open-source machine learning projects, patents, technical publications, or applied research.