Dear Candidates,
Please find the below JD for the role of Data Scientist (Lean Six Sigma Black Belt or Six Sigma Black Belt).
Role - Data Scientist (Lean Six Sigma Black Belt or Six Sigma Black Belt - Active Certification Mandatory)
Location - Denver ,Co (In-person interview )
Visa - GC and USC, H1
Team - Team: Operational Analytics & Insight (OAI)
Position Summary
The
Senior Data Scientist will serve as a technical lead in the development
of advanced analytical solutions. This role uniquely combines
Generative AI innovation with Six Sigma operational rigor to drive
measurable improvements across Spectrum’s network and customer
ecosystems. You will not only build models but also optimize the
processes they inhabit to ensure maximum ROI and statistical stability.
Key Responsibilities
- Generative
AI Strategy: Lead the research and implementation of Large Language
Models (LLMs) and Retrieval-Augmented Generation (RAG) to automate
complex business workflows and enhance internal knowledge management
systems.
- Operational
Excellence (Black Belt): Apply DMAIC (Define, Measure, Analyze,
Improve, Control) methodology to the data science lifecycle. Identify
root causes of operational inefficiencies and deploy AI solutions to
mitigate them.
- Advanced
Modeling: Build, validate, and deploy high-impact predictive models
(Churn, CLV, Propensity) using Python, PyTorch, and Scikit-learn.
- Process
Optimization: Utilize Black Belt principles to reduce "waste" in data
pipelines, improving model training speed and inference efficiency
within Databricks/AWS.
- Stakeholder
Storytelling: Act as a bridge between technical AI labs and executive
leadership, translating complex neural network outputs into Six
Sigma-validated business cases.
Required Technical Skills
- GenAI Stack: Experience with LangChain, LlamaIndex, Vector Databases (Pinecone/Milvus), and fine-tuning open-source models.
- Data Engineering: Expert-level SQL and PySpark for grooming large-scale datasets.
- Statistical
Control: Deep understanding of Design of Experiments (DoE), hypothesis
testing, and Statistical Process Control (SPC) to monitor model drift
and performance.
- MLOps: Proficiency in versioning and deploying models in cloud environments (Azure/AWS).
Education & Certifications
- Education: Master’s or PhD in a quantitative field (Statistics, CS, Engineering).
- Certification: Lean Six Sigma Black Belt or Six Sigma Black Belt(Active certification mandatory).
- Experience: 5-7+ years of experience in a data-driven environment with a proven track record of leading AI initiatives.
Regards,