IT - Technology Lead | data science | Machine LearningSysMind • Kansas City, Missouri, United States
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IT - Technology Lead | data science | Machine Learning
SysMind
- Kansas City, Missouri, United States
- Kansas City, Missouri, United States
Über
Location & Reporting Address St Louis, MO 63131 (Onsite)
Contract Duration 12 months
Rate Market rate – maximum market rate per hour
Start Date 13 Mar 2026
Visa Independent candidates only
Must Have Skills Python ML Ops Generative AI LLMs Prompt Engineering NLP
Nice to Have Skills AWS ETL
Responsibilities 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.
Required Qualifications 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 NoSQL 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.
Preferred Qualifications Experience with AWS services (S3, EMR, Lambda, Glue, SageMaker). 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.
Minimum Years of Experience 8 years
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Sprachkenntnisse
- English
Hinweis für Nutzer
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