Senior Data Scientist

This 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.