Data Scientist - Machine Leaning Model (5-10 yrs)

JOB OVERVIEW :

- 5+ years in the distribution or Healthcare industry and deep knowledge of their business practices

- 4+ years of proven Machine Learning experience and involvement in packaged platform delivery and management.

- Strong working knowledge of a variety of machine learning techniques (Regression, Clustering, Decision Tree, Probability Networks, Neural Networks, Bayesian models etc.) along with Generative AI (preferable with Plam 2 or Gemini Pro)

- Experience with Machine Learning and related technologies such as Python, Tensorflow, Torch, Amazon SageMaker, Jupiter Notebooks.

- Understanding of cloud data engineering and integration concepts.

- Working experience with automated deployment and orchestration (CI/CD, Docker)

- Working experience with GCP services (GCE, GKE)

- Working experience in designing and optimizing ML models using GCP Vertex AI services

- Experience in Supervised and Unsupervised Machine Learning including Classification, Forecasting, Anomaly Detection, Pattern Detection, Text Mining, using variety of techniques such as Decision trees, Time Series Analysis, Bagging and Boosting algorithms, Neural Networks, Deep Learning.

- Excellent programming skills in Python and PySpark

- Have implemented ML Ops on Google's GCP with particular focus on automated deployment and ensuring optimized performance.

- Have maintained and optimized the ML models developed by Data Scientists and ensured seamless deployment in different environments while enabling model tracking, model experimentation and model monitoring.

- Experience in using workflow management tools such as Airflow

- Experience of version control system (GitHub)

- Strong understanding of RESTful APIs and / or data streaming a big plus

- Google Cloud Platform certification is a plus

- Prefer experience with MLOps Agile practices

- Strong verbal and written communication skills especially relevant to Run & Sustain