Overview:
Responsibilities:
- Build and deploy advanced machine learning models & design robust analytics engines for high-end digital products
- Create repeatable, interpretable, dynamic and scalable models that are seamlessly incorporated into analytic data products
- Document code, results, and details of the approaches in a systematic way to promote knowledge sharing and code re-use
- Collaborate with other Data Scientists to ideate and solve complex issues pertinent to data ingestion /curation, modality integration, model performance, model generalization
- Effectively communicate the analytics approach and how it will meet and address objectives to business partners (both technical and non-technical audiences)
- Stay up-to-date with latest industry trends and advances in data science, machine learning and generative AI
- Actively contribute to a culture of transparency, learning, and innovation, promoting an open mindset across the organization
- Advocate and educate on the value of data driven decision making focusing on the “how and why” of solving problems
- Lead analytic approaches, integrating work into applications and tools with data engineers, business leads, analysts and developers
- Engineer features by using your business acumen to find new ways to combine disparate internal and external data sources
- Share your passion for Data Science with broader enterprise community; identify and develop long-term processes, frameworks, tools, methods and standards
- Collaborate, coach, and learn with a growing team of experienced Data Scientists
Skills / Qualifications:
- Bachelor’s in Data Science, Computer Science, Engineering, Statistics and 5+ years experience required OR MS or PhD in quantitative discipline and demonstrated Data Science skill set, plus 2+ years work experience
- Extensive knowledge and expertise in building, testing, and deploying complex machine learning models that demonstrate high accuracy and robustness
- Strong proficiency in Python, PostgreSQL and Azure Cloud Stack
- Experience with tuning, deployment, monitoring and maintenance of models in production desired
- Expertise in using and implementing cutting-edge machine learning algorithms, frameworks, and libraries, such as PyTorch, Keras, Tensorflow to solve clustering, classification, regression, and optimization problems on large scale data sets
- Hands-on experience with modern NLP methods required. Specifically:
- Transformers models (ex. BERT), LLMs, RAG & Fine-Tuning, OpenAI Stack, Langchain etc.
- Experience with Big Data technologies a plus —PySpark, H20.ai, Cloud AI platforms, Kubernetes
- Must be able to translate business requirements into analytical problems
- Must have proven ability to merge and transform disparate internal & external data sets together to create new features
Company Overview:
Diversity Statement: