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Data Scientist

Siri Infosolutions Inc
Minneapolis, MN Contractor
POSTED ON 10/14/2026 CLOSED ON 10/15/2026

What are the responsibilities and job description for the Data Scientist position at Siri Infosolutions Inc?

Job Title: Data Scientist – Machine Learning & Predictive Analytics

Location: Minneapolis, MN
Work Arrangement: Onsite – Day 1
Experience: 5 Years

Job Description

We are seeking an experienced Data Scientist with strong expertise in statistical analysis, machine learning, predictive modeling, and advanced analytics. The ideal candidate will have hands-on experience analyzing complex datasets, developing machine learning models, identifying patterns, and translating data-driven insights into actionable business recommendations.

Required Skills

  • 5 years of hands-on Data Science experience.

  • Strong knowledge of statistical methods, mathematical techniques, and machine learning algorithms.

  • Experience with supervised and unsupervised learning.

  • Proficiency in Python or R for data analysis and modeling.

  • Strong SQL skills for data extraction and manipulation.

  • Experience in exploratory data analysis (EDA), data cleaning, preprocessing, and feature engineering.

  • Hands-on experience developing, implementing, and evaluating predictive models.

  • Knowledge of model validation, performance metrics, and evaluation techniques.

  • Experience with data visualization and analytical reporting.

  • Strong analytical, problem-solving, and communication skills.

Key Responsibilities

  • Analyze large and complex datasets to identify trends, patterns, and business insights.

  • Develop, implement, and evaluate machine learning algorithms and predictive models.

  • Apply statistical techniques to solve real-world business problems.

  • Perform data preprocessing, feature engineering, and exploratory data analysis.

  • Select appropriate algorithms based on business objectives and data characteristics.

  • Evaluate model performance using statistical metrics and validation techniques.

  • Present findings and recommendations to technical and business stakeholders.

  • Collaborate with cross-functional teams to support data-driven decision-making.

  • Document methodologies, assumptions, model results, and recommendations.

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