Key Responsibilities
- Develop and optimize machine learning and statistical models for anomaly detection, predictive analytics, and process optimization.
- Perform advanced data analysis, visualization, Bayesian modeling, and Monte Carlo simulations.
- Implement real-time monitoring solutions for data streams and system performance.
- Analyze large and complex datasets to identify trends, anomalies, and root causes.
- Improve alert accuracy and reduce false positives through model tuning and threshold optimization.
- Collaborate with cross‑functional teams including product management, engineering, manufacturing, and IT to deliver scalable data solutions.
- Apply data science and software engineering best practices for robust and reproducible model deployment.
- Support data quality, security, and automated analytics pipelines.
- Research and integrate emerging AI/Generative AI capabilities to improve operational efficiency and experimentation.
- Communicate technical findings and recommendations to both technical and non‑technical stakeholders.
Required Skills
- Strong expertise in Python and data science libraries such as Pandas, scikit‑learn, and Jupyter.
- Experience with machine learning, statistical modeling, optimization techniques, and feature engineering.
- Knowledge of data mining, anomaly detection, and predictive analytics.
- Experience working with large and unstructured datasets.
- Familiarity with AWS, Docker, Git, and software development best practices.
- Working knowledge of relational databases such as PostgreSQL.
- Strong analytical, problem‑solving, and communication skills.
- Ability to manage multiple projects in a fast‑paced environment.
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