Lead Data Science - R01551285
About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction.
Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.
Data Science Lead
- ML - classification, regression, clustering, boosting, Time series - (ARIMA, xgboost, exponential smoothening, moving average), causal ML, random forest, decision trees and LLM is good to have.
- Location: Chicago, IL - Hybrid with 2-3 days on-site in a week
- Looking for a Data Science Lead with expertise in machine learning, time series forecasting, causal ML, cloud data platforms, and AI technologies:
- Programming: Proficiency in Python and PySpark for data manipulation and ML model development.
- ML Expertise: Strong experience in machine learning, especially:
- Time Series Forecasting (ARIMA, Prophet, LSTM, XGBoost, etc.)
- Causal ML (e.g., uplift modeling, propensity scoring, causal impact analysis)
- Deep Learning: Neural networks (CNNs, RNNs, LSTMs), transformers, embeddings
- NLP: Language modeling, embeddings (Word2Vec, BERT, GPT), text classification, summarization
- SQL: Strong skills in writing complex queries for data extraction and transformation.
- Cloud Platforms: Experience with AWS (S3, SageMaker, Lambda, Glue, etc.)
- Databricks: Familiarity with Lakehouse architecture, notebook development, and collaborative ML workflows.
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