The company is looking to solve complex business challenges across industries by developing impactful advanced analytics and AI solutions, optimizing code, and uncovering patterns in data to help clients stay competitive, transform operations, and achieve lasting improvements.
Requirements
- Professional experience in applying machine learning and data mining techniques to real problems with copious amounts of data
- Development experience (focus on machine learning): SQL and Python’s data-science stack; proficiency with Spark/PySpark for distributed workloads.
- Technologies you may encounter include Airflow, Databricks, Dask/RAPIDS, containerization with Docker and Kubernetes, and the major clouds (AWS, GCP, Azure, Oracle)
- GenAI experience a plus: parameter-efficient tuning, RAG architectures, vector-store technologies, LLM evaluation
- Optimize inference latency and cost through parameter-efficient tuning, quantization, and accelerated serving stacks
Responsibilities
- Translate business questions into analytical approaches and select the right techniques for each problem
- Conduct exploratory data analysis
- Design, implement, and evaluate models—from traditional machine learning to deep learning to LLMs
- Build production-grade RAG pipelines and assess LLM output quality / hallucinations
- Deploy models via APIs or batch pipelines, write unit tests, and set up monitoring dashboards to track performance and drift
- Document assumptions, communicate results in clear, actionable language, and collaborate with engineers to integrate solutions into user-facing applications.
- Build models which are accurate, explainable, and free from bias
Other
- U.S. Citizenship is required
- Bachelor’s degree in computer science with 2+ years of professional experience OR Masters or PhD a discipline such as computer science, mathematics, statistics or electrical engineering
- Exceptional time management to meet your responsibilities in a complex and largely autonomous work environment
- Strong communication skills, both verbal and written, in English and local office language(s), with the ability to adjust your style to suit different perspectives and seniority levels
- Willingness to travel
