Senior-level Machine Learning Engineer​/Data Scientist

BCforward is a leading global IT consulting and workforce solutions firm providing services and support to Fortune 500 and government clients. Founded in 1998, BCforward has grown with our customers needs into a full-service business solutions provider. With delivery centers and offices across North America and India, we take pride in building long-term relationships and delivering excellence through innovation, collaboration, and integrity.

Job Title:

Senior-level Machine Learning Engineer / Data Scientist

Location:

Plano, TX

Duration:
Contract - 11 months (potential contract-to-hire)

Pay Range: $73.67/hr (W2)

Job

We are seeking a Senior-level Machine Learning Engineer / Data Scientist to join our dynamic team. The ideal candidate will have strong experience in Python-based model development, feature engineering, evaluation, and deployment and a proven ability to deliver scalable classification models from data exploration through production implementation.

• Design, build, train, validate, and tune supervised machine learning classification models using structured, behavioral, and transactional data.
• Perform hands‑on data exploration, cleansing, transformation, and feature engineering to create reliable inputs for model development.
• Develop repeatable Python-based workflows, scripts, and pipelines for dataset preparation, model training, evaluation, and scoring.
• Evaluate classification performance using precision, recall, F1 score, ROC‑AUC, and confusion matrices, and iterate based on false positive and false negative analysis.
• Package and deploy models into scalable production or near-production environments in partnership with engineering teams.
• Troubleshoot data quality, pipeline, model performance, and integration issues across development, testing, and deployment phases.
• Create clear technical documentation for data assumptions, feature logic, model design, validation results, implementation details, and operations.
• Collaborate with engineers, analysts, and stakeholders to ensure solutions are maintainable, explainable, and aligned to measurable outcomes.

• Master's or PhD in computer science, statistics, mathematics, engineering, data science, or a related discipline.
• 5-10 years of hands‑on data science or machine learning engineering experience in technical environments.
• Strong Python development with pandas, Num Py, scikit-learn, XGBoost, or similar libraries.
• Deep understanding of supervised classification, feature engineering, model training, hyperparameter tuning, validation, threshold selection, and performance evaluation.
• Experience with large structured datasets, data cleansing and transformation, exploratory analysis, and repeatable preparation workflows.
• Ability to evaluate models using precision, recall, F1, ROC‑AUC, confusion matrices, and error analysis.
• Experience packaging, versioning, testing, and integrating models or scoring logic into application workflows.
• Strong technical communication and documentation skills.
• Experience using generative AI tools such as Microsoft Copilot and applying prompt engineering techniques.

• Classification models for fraud, risk, cybersecurity, authentication, customer behavior, or anomaly detection.
• MLOps practices including version control, automated testing, CI/CD, monitoring, and reproducible deployments.
• Real‑time or near real‑time inference design and integration with production decisioning workflows.
• Distributed processing and cloud platforms such as Spark, Databricks, Hadoop, AWS, or Azure.
• Working knowledge of SQL for data extraction, joins, profiling, and validation.
• Dashboards, reporting, or visualizations for model…