Data Scientist II 4P/573

4P Consulting

  • Atlanta, Georgia
  • 30+ days ago

    Highlights

    The Data Scientist II is responsible for leveraging large and complex datasets to uncover insights, develop predictive models, and drive data-informed decision-making across the organization. The ideal candidate is a strategic thinker who can translate business challenges into scalable analytical solutions while mentoring junior team members.

    Numbers & Facts

    LocationAtlanta, Georgia

    Description

    Data Scientist II

    Experience Level: 5–10 Years
    Location: Atlanta, GA
    Contract- 1 Year

    Client- Southern Company

    Position Overview

    The Data Scientist II is responsible for leveraging large and complex datasets to uncover insights, develop predictive models, and drive data-informed decision-making across the organization.

    This role requires strong expertise in advanced analytics, machine learning, statistical modeling, and data engineering principles. The ideal candidate is a strategic thinker who can translate business challenges into scalable analytical solutions while mentoring junior team members.

    Key Responsibilities

    Data Analysis & Insight Generation

    • Collect, clean, and analyze complex datasets

    • Identify trends, patterns, and actionable insights

    • Apply statistical techniques to support data-driven decisions

    Predictive Modeling & Machine Learning

    • Develop and deploy machine learning models to predict future trends and outcomes

    • Apply regression, clustering, classification, and advanced modeling techniques

    • Build and optimize algorithms such as:

      • Decision Trees

      • Random Forests

      • Neural Networks

      • Gradient Boosting models

    Feature Engineering & Model Optimization

    • Engineer and select relevant features to improve model performance

    • Fine-tune model parameters and validate predictive accuracy

    • Ensure models are scalable and production-ready

    Model Deployment & Production Support

    • Deploy machine learning models into production environments

    • Support real-time decision-making applications

    • Monitor model performance and retrain as needed

    Data Visualization & Communication

    • Develop dashboards and visualizations using Tableau, Power BI, or Python libraries (Matplotlib, Seaborn, etc.)

    • Communicate insights effectively to technical and non-technical stakeholders

    Hypothesis Testing & Experimentation

    • Design and analyze A/B tests

    • Conduct hypothesis testing and provide statistical validation

    • Measure business impact of changes and enhancements

    Data Integration & Collaboration

    • Collaborate with IT and database teams to access and integrate data sources

    • Work with cross-functional teams (engineering, business analysts, domain experts)

    • Align data science initiatives with strategic business objectives

    Governance & Ethics

    • Ensure ethical data practices and compliance with data privacy regulations

    • Maintain documentation and transparency in model development

    Leadership & Development

    • Mentor junior data scientists and analysts

    • Contribute to best practices and data science methodologies

    • Stay current with emerging tools, technologies, and industry trends

    Required Qualifications

    • Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field

    • 5–10 years of experience in data science, machine learning, and statistical analysis

    • Proficiency in Python, R, or Julia

    • Strong understanding of machine learning algorithms and their applications

    • Experience with SQL and database querying

    • Experience with data visualization tools (Tableau, Power BI, or Python libraries)

    • Strong analytical, problem-solving, and critical-thinking skills

    • Excellent written and verbal communication skills

    Preferred Qualifications

    • Master’s or Ph.D. in a quantitative field

    • Experience with big data technologies (Hadoop, Spark)

    • Experience with distributed computing frameworks

    • Experience deploying models in cloud environments

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