Description :

Looking for a Sr. Data Scientist with 5+ years of work experience training and deploying ML models. In addition, you should have at least three years of industry experience developing and deploying ML models for predictive and condition-based maintenance in production.

Glassbeam provide opportunities to work on challenging problems and make an impact. Learn and develop innovative products with the latest AI and ML technologies. We encourage you to try new ideas.

A few examples of the problems that you will be addressing with Data Science, ML and LLMs :

- Increase Service Engineer/Technical Support Specialist productivity

- Reduce Mean-Time-to-Repair

- Increase First-Time-Fix-Rate

- Increase Mean-Time-Between-Failures

- Reduce unplanned device downtime

- Increase device utilization

You will own the complete end-to-end ML workflow :

- Develop domain expertise and in-depth understanding of different types of medical devices

- Understand and conduct EDA on medical device operational (non-patient) data

- Formulate a business problem into a ML / Data Science problem

- Determine the best ML algorithm and statistical technique for a given problem and dataset

- ETL, cleanse and prepare system data for Machine Learning

- Train, evaluate, deploy, monitor, and manage ML models in production

The ideal candidate is an experienced Data Scientist highly skilled in :

- Framing a business problem into a ML / Data Science problem

- Analyzing and preparing complex messy unstructured data for ML

- Training, evaluating, and deploying Failure Prediction and Anomaly Detection models

Minimum (must-have) :

- 5+ years' work experience as a Data Scientist developing production-grade ML models

- 3+ years' work experience training and deploying Failure Prediction models in production

- 2+ years' work experience training and deploying Anomaly Detection models in production

- In-depth knowledge of ML algorithms, frameworks, libraries, and related tools

- Excellent exploratory data analysis, data prep and feature engineering skills

- Strong knowledge of statistics and time series data analysis

- 2+ years' work experience using Python and SQL

- Masters or Ph.D. in Statistics, Computer Science, or another quantitative field

Plus (not a must-have but desirable) :

- MLFlow

- Vertica, Delta Lake

- Tableau, Superset