Senior Data Scientist
NewBe an early applicantThis is a remote position.
- 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.
- 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.
- 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.