Predictive Analytics Engineer

Select Minds LLC•Dallas, TX

22h•$100,000 - $120,000•Hybrid

About The Position

We are seeking a talented Data Scientist to join our growing analytics team in Dallas, TX. The ideal candidate will have experience building predictive models, developing machine learning solutions, and transforming complex datasets into actionable business insights. You will work closely with product, engineering, and business stakeholders to drive data-driven decision making and improve business outcomes.

Requirements

2+ years of professional experience as a Data Scientist.
Strong programming skills in Python or R.
Advanced SQL skills with experience querying complex relational databases.
Hands-on experience with machine learning frameworks such as: Scikit-Learn, TensorFlow, PyTorch
Experience with statistical modeling, predictive analytics, and feature engineering.
Strong understanding of: Regression Analysis, Hypothesis Testing, Probability & Statistics
Experience with data visualization tools such as: Tableau, Power BI, Matplotlib
Excellent analytical, problem-solving, and communication skills.

Nice To Haves

Experience working with cloud platforms such as AWS, Azure, or GCP.
Knowledge of data pipelines, ETL processes, and big data technologies.
Experience with MLOps, model deployment, and monitoring is a plus.
Familiarity with version control systems such as Git.

Responsibilities

Collect, clean, and integrate structured and unstructured data from multiple sources.
Build, train, validate, and deploy machine learning models for predictive analytics.
Design and execute A/B testing and statistical experiments to optimize products and business processes.
Develop automated dashboards and reports to monitor KPIs and business performance.
Perform exploratory data analysis to identify trends, patterns, and opportunities.
Present analytical findings and recommendations to technical and non-technical stakeholders.
Collaborate with cross-functional teams including Product, Engineering, Marketing, and Business Operations.
Optimize existing data science workflows and improve model performance.
Ensure data quality, governance, and best practices throughout the analytics lifecycle.