Design, build, evaluate, and deploy production-grade AI and machine learning models for complex business challenges. Responsibilities include exploratory data analysis, feature engineering, statistical experimentation, advanced modeling across NLP, computer vision, forecasting, and recommendations, and adapting Transformers, LLMs, and graph neural networks. The role also involves model interpretability, responsible AI, MLOps collaboration, stakeholder communication, and integrating current research into scalable production systems.
This is a remote position.
We are seeking a Senior Data Scientist to design, build, and deliver world‑class AI/ML models that solve complex, high‑impact business challenges. This role involves working with large, complex datasets, applying advanced mathematical and statistical techniques, and deploying production‑ready AI models that deliver measurable business outcomes. The ideal candidate brings deep expertise in mathematical modeling, machine learning, and real‑world problem solving, with the ability to bridge research rigor and business intuition.
Key Responsibilities
- Translate complex business objectives into mathematical formulations, predictive models, or optimization frameworks.
- Conduct exploratory data analysis (EDA), feature selection, and feature engineering using advanced statistical techniques.
- Build, train, tune, and evaluate machine learning and deep learning models across domains such as NLP, Computer Vision, Forecasting, and Recommendation Systems.
- Research, prototype, and adapt state‑of‑the‑art models including Transformers, LLMs, and Graph Neural Networks for client use cases.
- Apply model interpretability techniques (SHAP, LIME, Explainable AI) and explain insights to non‑technical stakeholders.
- Design and execute statistical experiments, including A/B testing and hypothesis testing, to validate model impact in production.
- Deploy models via APIs or lightweight serving systems and collaborate with engineering teams on MLOps and productionization.
- Stay current with academic and industry research, continuously integrating new algorithms and techniques into production systems.
Requirements
- 8+ years of experience in Data Science, Machine Learning, or Applied Mathematics roles.
- Strong academic foundation in Statistics, Probability, Linear Algebra, Optimization, and Calculus.
- Advanced proficiency in Python, including pandas, NumPy, scikit‑learn, TensorFlow, PyTorch, and HuggingFace.
- Proven experience building production‑grade AI/ML models with demonstrable business impact.
- Strong expertise in model evaluation metrics (ROC‑AUC, F1, Precision‑Recall, cost‑sensitive metrics).
- Hands‑on experience with cloud ML platforms such as SageMaker, Vertex AI, or Azure ML.
- Solid understanding of model fairness, bias detection, and responsible AI practices.
Good to Have / Preferred Skills
- Experience working with LLMs, NLP, Computer Vision, or Time‑Series Forecasting at scale.
- Publications in peer‑reviewed conferences or strong applied research background.
- Familiarity with AutoML, Reinforcement Learning, and Bayesian Optimization.
- Strong ability to work with messy, ambiguous real‑world datasets.
- Scientific rigor combined with business intuition for modeling decisions.
- Passion for building interpretable, scalable, and high‑impact AI models.
Soft Skills
- Excellent analytical and critical‑thinking skills.
- Ability to communicate complex concepts clearly to technical and business stakeholders.
- Strong ownership and accountability.
- Curiosity and continuous‑learning mindset.
- Ability to work independently in a remote, distributed team environment.
Benefits
- Competitive compensation as per industry standards.
- Opportunity to work on cutting‑edge AI/ML problems with real business impact.
- Remote‑first work environment with global collaboration.
- Strong exposure to advanced AI research and enterprise‑scale deployments.
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