Senior AI Engineer/Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Lead comprehensive analysis of extensive, intricate, multi-source datasets to uncover patterns influencing model inputs.

• Identify, gather, cleanse, validate, and transform all necessary data for prediction model utilization.

• Design and uphold scalable, production-ready data pipelines (for training, validation, and inference).

• Conduct thorough exploratory data analysis (EDA), data profiling, and quality audits to maintain model-ready data standards.

• Architect, train, evaluate, and refine machine learning models — including supervised, unsupervised, and reinforcement learning.

• Take ownership of feature engineering: selection, extraction, transformation, and dimensionality reduction.

• Employ advanced methodologies: deep learning, natural language processing (NLP), time-series forecasting, and ensemble techniques.

• Benchmark, conduct A/B testing, and monitor models in production; drive ongoing performance enhancement.

• Deploy models through REST APIs (FastAPI/Flask); ensure both reproducibility and scalability.

• Self-manage from problem definition through to solution delivery without requiring guidance.

• Translate vague business challenges into clear, actionable data science problem statements.

• Effectively communicate model outcomes and data insights to both technical and non-technical audiences.

• Document all experiments, methodologies, and results — ensuring audit-ready and reproducible processes.

• Advocate for best practices throughout the data science lifecycle and mentor junior colleagues.

⛳️ Requirements

• B.S./M.S./Ph.D. in Computer Science, Statistics, Mathematics, or a similar quantitative discipline (Master's/Ph.D. is highly preferred).

• Over 5 years of practical data science experience, with at least 2 years focused on delivering production-quality ML models.

• Demonstrated ability to independently manage and deliver complete data science projects.

• A portfolio showcasing innovation in predictive modeling and quantifiable business impact.

• Kaggle rankings, research publications, or contributions to open-source ML projects are significant advantages.

• Experience in a dynamic, data-driven, decision-making environment.

• Proficiency in statistics (Bayesian inference, hypothesis testing, regression, distributions).

• Knowledge of linear algebra, calculus, and probability as they pertain to ML model development.

• Familiarity with supervised and unsupervised learning, anomaly detection, and clustering techniques.

• Expertise in time-series analysis and forecasting methods: ARIMA, Prophet, LSTM.

• Advanced Python skills: NumPy, Pandas, Scikit-learn, Statsmodels, Matplotlib, Plotly.

• Advanced SQL capabilities: window functions, CTEs, and query optimization.

• Proficient in Git/GitHub; CI/CD for ML; and MLOps utilizing MLflow or Kubeflow.

• Experience with Docker and Kubernetes for model containerization and serving.

• Familiarity with TensorFlow and/or PyTorch for deep learning frameworks.

• Knowledge of XGBoost, LightGBM, and CatBoost for gradient boosting and ensemble strategies.

• Proficient in using Hugging Face Transformers for NLP, LLMs, and fine-tuning processes.

• Understanding of SHAP and LIME for model explainability and interpretability.

• Familiarity with LLMs, Generative AI, and Prompt Engineering is a strong advantage.

• Experience with AWS (SageMaker, S3, Glue), GCP (Vertex AI, BigQuery), or Azure ML.

• Knowledge of Apache Spark/PySpark for distributed data processing.

• Experience with Airflow or Prefect for pipeline orchestration.

🏝️ Benefits

• Health insurance

• 401(k) matching

• Flexible work hours

• Paid time off

• Remote work options