Data Scientist/Machine Learning Engineer

WE'RE HIRING: DATA SCIENTIST / MACHINE LEARNING ENGINEER

We're looking for an experienced Data Scientist / Machine Learning Engineer to design, develop, deploy, and maintain predictive machine learning models using enterprise data.

This role will work closely with Data Engineering, IT, Operations, and Business teams to transform a growing Snowflake data environment into actionable predictive intelligence. Initial priorities include predictive scoring, propensity-to-pay, account prioritization, segmentation, and next-best-action modeling, with opportunities to expand ML applications across operations, workforce analytics, sales, client analytics, and other enterprise use cases.

Position Details

• Position: Data Scientist / Machine Learning Engineer
• Employment Type: Full-Time
• Rate:$13/hour
• Timezone:US Time Zone
• Schedule: TBD

Key Responsibilities

• Collect, clean, transform, and analyze large datasets for machine learning projects
• Develop and maintain data-processing scripts and automated data pipelines
• Prepare structured and unstructured data for machine learning models
• Design, develop, validate, deploy, and maintain predictive ML models
• Develop models for:
• Propensity scoring
• Account prioritization
• Segmentation
• Next-best-action
• Treatment optimization
• Establish model benchmarks and conduct back-testing against existing scoring methods and actual business outcomes
• Monitor model performance and implement retraining, versioning, and continuous improvement processes
• Develop feature engineering strategies using enterprise data, including transaction history, payment behavior, digital engagement, communication outcomes, call transcripts, and third-party data
• Perform data validation, quality checks, and troubleshooting
• Integrate model scores and recommendations into operational systems through Snowflake, APIs, BI platforms, and other applications
• Partner with business leaders and domain experts to translate operational challenges into measurable ML use cases
• Define target variables and model objectives
• Develop methods to measure the financial and operational impact of deployed models, including lift and ROI
• Communicate model methodology, performance, limitations, and business implications to both technical and non-technical stakeholders

Required Technical Skills

• Python and SQL
• Pandas, NumPy, and Scikit-learn
• Supervised learning
• Classification and ranking/scoring models
• Feature engineering
• Statistical model evaluation
• Data structures and algorithms
• Data processing and pipeline development
• Snowflake or comparable modern cloud data platforms
• Model lifecycle management, including testing, deployment, monitoring, retraining, and version control

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Information Technology, Mathematics, Statistics, Engineering, or a related field
• Advanced degree is preferred but not required with appropriate experience
• Demonstrated experience developing, validating, and deploying machine learning models in a production business environment
• Strong analytical and problem-solving skills
• Ability to work effectively with data scientists, engineers, and business teams
• Ability to translate business objectives into measurable predictive modeling problems
• Strong communication skills, including the ability to explain technical results to executive and operational stakeholders

Preferred Qualifications

Experience with any of the following is a strong advantage:

• TensorFlow or PyTorch
• XGBoost, LightGBM, or similar ML frameworks
• Spark, Databricks, Airflow, or similar data technologies
• AWS, Azure, Google Cloud, or Snowflake
• APIs and data integration
• Git and CI/CD practices
• Docker
• NLP, Generative AI, or other ML applications
• Snowflake ML, Snowpark, Cortex, or related Snowflake AI capabilities

Industry & Business Experience – Preferred

Experience in any of the following environments is highly valued:

• Accounts receivable management
• Collections
• Consumer or commercial credit
• Lending
• Debt purchasing
• Financial services

Experience developing models for propensity-to-pay, default risk, collectibility, segmentation, account prioritization, treatment optimization, or next-best-action is a major advantage.

Additional valuable experience includes:

• Using customer/contact behavior, payment history, digital engagement, or communication data in predictive models
• Applying NLP or ML to call transcripts and speech analytics
• Workforce analytics, including hiring, employee performance, retention, or workforce optimization
• Predictive analytics for sales, client analytics, revenue forecasting, or operational optimization
  

Technology Stack

Programming: Python, SQL, R, Java/Scala

Data: ETL/ELT, data cleaning, data pipelines, databases, feature engineering, Snowflake

Machine Learning: Scikit-learn, TensorFlow, PyTorch, XGBoost/LightGBM

Cloud: AWS, Azure, Google Cloud, Snowflake

Tools: Git, Docker, Spark, Airflow, Databricks

Business: Predictive analytics, experimentation, model benchmarking, ROI/lift analysis

The ideal candidate combines strong technical ML skills with the ability to understand business problems, work across technical and operational teams, and clearly communicate how predictive models can create measurable business value.

Apply now and join the team!