IT – Technology Lead | data science | Machine Learning

Job Title

Technology Lead | data science | Machine Learning – Data Scientist

St Louis, MO 63131 (Onsite)

12 months

Market rate – maximum market rate per hour

13 Mar 2026

Independent candidates only

• Python
• ML Ops
• Generative AI
• LLMs
• Prompt Engineering
• NLP

• AWS
• ETL

• Lead the full ML development lifecycle: problem framing, hypothesis formulation, feature engineering, model development, validation, deployment, and monitoring.
• Develop, test, and optimize machine learning models including:
  • Supervised & unsupervised learning
  • Statistical modeling and forecasting
  • Natural Language Processing (NLP)
  • Generative AI techniques for automation and insight extraction
  • Graph/network analytics for analyzing network behaviors and relationships
• Build advanced anomaly detection, predictive maintenance, and risk scoring models for network security and operational efficiency.
• Conduct large‑scale exploratory data analysis (EDA) to identify trends, data quality issues, and opportunities for automation.
• Define and implement model evaluation and A/B testing strategies.
• Collaborate with ML engineering teams to operationalize models using MLOps best practices.
• Communicate complex analytical findings through clear narratives, visualizations, and presentations tailored to technical and non‑technical audiences.
• Design, develop, and maintain scalable, fault‑tolerant ETL pipelines using Spark to support analytics and machine learning workloads.
• Implement monitoring, alerting, and automated recovery mechanisms to ensure data pipeline reliability.
• Build robust feature pipelines that enable real‑time and batch ML processing.
• Integrate data from a wide range of sources including:
  • APIs
  • Flat files
  • Relational databases
  • Distributed file systems (HDFS/S3)
• Support continuous integration and continuous delivery (CI/CD) workflows for data and ML components.
• Partner with engineering, operations, security, and business teams to embed machine learning solutions into production systems.
• Provide mentorship to junior data scientists and analysts.
• Evangelize data science best practices across the organization and contribute to internal frameworks, tools, and standards.
• Help educate teams on analytic techniques, statistical reasoning, and responsible AI practices.

• Strong communication, presentation skills, and ability to translate analytics into business value.
• Expertise in programming languages commonly used in data science:
  Python (primary), Scala or Java (preferred for ETL/engineering).
• Proven experience with Spark and large‑scale distributed data processing.
• Deep understanding of:
  Statistical modeling, hypothesis testing, experimental design, causality and multicollinearity.
• Strong SQL skills and experience with relational and No
  
  SQL databases.
• Expertise across a wide range of ML methodologies:
  Regression, classification, clustering, Time‑series forecasting, Bayesian methods, NLP and text analytics, Graph analytics.
• Experience with data preprocessing, feature engineering, and EDA.
• Familiarity with data architectures such as data lakes, warehouses, and marts.
• Demonstrated ability to continuously learn, adapt, and share knowledge.

• Experience with AWS services (S3, EMR, Lambda, Glue, Sage Maker).
• Prior exposure to Generative AI, LLMs, prompt engineering, or building AI‑driven automation systems.
• Experience with Linux‑based systems.
• Background in text mining, document classification, or large‑scale unstructured data processing.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering, Operations Research, or a related field.
• Master’s degree with 6 years or Bachelor’s degree with 8 years of relevant work experience.

8 years