Senior Data Scientist (Six Sigma Black Belt)

Position: Senior Data Scientist (Lean Six Sigma Black Belt or Six Sigma Black Belt (Active certification mandatory).

Location: Denver CO

Duration: 6+ months

In Person interview is must

Data Scientist (Lean Six Sigma Black Belt or Six Sigma Black Belt(Active certification mandatory).

Note:

Should have experience Operational Analytics & Insight (OAI) team.

Should have experience within (Operational Analytics & AI)

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:

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: Overall 12 plus years of experience in a data-driven environment with a proven track record of leading AI initiatives