Job ID: JOB_ID_4194
Job Description
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.
Data Engineering & ETL
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:
- APIs
- Flat files
- Relational databases
- Distributed file systems (HDFS/S3)
Support continuous integration and continuous delivery (CI/CD) workflows for data and ML components.
Collaboration & Leadership
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 the development of 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.
- Bachelors degree in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering, Operations Research, or a related field.
- Masters degree with 6+ years or Bachelors degree with 8+ years of relevant work experience.
Special Requirements
F2F interview
Compensation & Location
Salary: $50 – $70 per year
Location: St. Louis, MO
Recruiter / Company – Contact Information
Email: swati@kanandcorp.com
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