Data Scientist

R Systems, Inc.
Denver, United States of America
6 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Denver, United States of America

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Artificial Neural Networks
Azure
Big Data
Information Engineering
Statistical Hypothesis Testing
Python
Knowledge Management
Open Source Technology
Statistical Process Control (SPC)
SQL Databases
Management of Software Versions
Cloud Platform System
PyTorch
Large Language Models
Generative AI
PySpark
Scikit Learn
Data Analytics
Machine Learning Operations
Data Pipelines
Databricks

Job description

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., * 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.

Requirements

  • 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(

Experience: 5-7+ years of experience in a data-driven environment with a proven track record of leading AI initiatives

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