Data Scientist

Job Overview: Pay Range $45.96hr - $50.96hr Requirement/Must Have: 1+ years of experience in data analysis, statistical modeling, and ML development using Python (pandas, numpy, scikit-learn, scipy, curve fitting, object-oriented programming). Ability to build multi-scenario models to test assumptions and evaluate alternative planning outcomes. Foundational to intermediate experience with ML frameworks and methodologies (scikit-learn, XGBoost, or similar). Understanding of model validation metrics (R , MAE, RMSE, cross-validation, custom scoring functions). Proficiency in SQL for querying, joining tables, data manipulation, and interpreting complex queries. Understanding of statistical modeling, hypothesis testing, and experimental design. Ability to independently explore enterprise datasets and identify patterns, gaps, and opportunities. Experience handling messy data, identifying inconsistencies, and standardizing formats across heterogeneous systems. Experience merging multiple datasets from various enterprise data sources (SAP, Salesforce, Databricks, ERP/CRM). Sharp eye for finding outliers, errors, and unusual patterns in structured and unstructured data. Understanding of data modeling concepts across heterogeneous systems. Experience developing models for scenario modeling and predictive use cases. Familiarity with Large Language Models (LLMs) and basic prompt engineering techniques for practical business applications. Ability to review existing dashboards, ML models, and reports to understand design patterns, business requirements, and underlying data sources. Strong capability to read and interpret complex SQL queries to understand data flows and business logic. Skills to trace data lineage, review prepared data sources, and comprehend underlying data structures. Experience working with Data Engineers to ensure data requirements are correctly implemented at pipeline and infrastructure level. Responsibilities: Analyze quality data from multiple enterprise systems (SAP, Salesforce, Databricks, Power BI, labor systems, finance data) to identify patterns, gaps, and opportunities for data-driven improvements. Work with Program Managers and/or Operations leaders to define which data assets are relevant for business use cases and specify how data from different systems should be accessed, interpreted, and used. Transform structured/unstructured datasets (often 100k+ rows) into actionable insights. Conduct data quality checks and identify/resolve data defects and abnormalities across enterprise platforms. Develop and validate Machine Learning models that support demand forecasting, scenario modeling, and predictive use cases for short-term and long-term business goals. Document analytical findings, model performance, and data definitions clearly to ensure transparency and reproducibility across the team. Experience working with Data Engineers to ensure data requirements are correctly implemented; ability to build and maintain Python-based data pipelines for ETL, model training, and automated forecasting workflows. Translate business technical data challenges into concrete data science and AI/ML problem statements, acting as the domain-aware bridge between Engineering/Operations and the Digital team. Leverage Large Language Models (LLMs) and prompt engineering to build intelligent tools that augment human decision-making and automate workflows. Design and execute scenario planning models to test business assumptions (demand forecasts, resource capacity, cost projections) and evaluate 'what-if' outcomes for strategic decision-making. Track project execution data across P6 (Primavera) and other project management systems, linking planning assumptions to actual execution performance. Support variance analysis between planned assumptions (forecast hours, budgets, timelines) and actual project execution data to identify gaps, root causes, and trends. Build automated tracking solutions that monitor assumption validity as projects progress through lifecycle stages (planning design execution closeout). Collaborate with Program Managers to refine planning assumptions based on execution learnings and historical pattern analysis. Provide data pipeline and data to build executive dashboards that visualize assumption-to-execution alignment, highlighting projects at risk due to assumption breakdown. Review and analyze existing dashboards, models, and data pipelines to understand design patterns, business requirements, and data flows. Read and interpret SQL queries, business logic, and semantic models embedded in current reports and analytical systems. Understand underlying data structures and prepared data sources to support maintenance and enhancement. Identify opportunities to optimize or consolidate existing reporting and modeling assets. Maintain consistency with established data standards and best practices. Translate complex data findings and model outputs into clear, actionable business insights for both technical and non-technical audiences. Resolve customer and internal user queries related to model outputs, data insights, or data defects. Support the Operations team in delivering centralized data analysis-based reporting solutions (including KPI), providing harmonized insights and KPIs to business stakeholders across global business lines. Collaborate closely with cross-functional Data analysts and Data engineers to ensure data requirements are correctly understood and implemented at pipeline and infrastructure level. Build and maintain a deep understanding of Semantic Data Models to ensure consistent data interpretation across applications and business systems. Stay current with the latest advancements in AI, ML, and data science, proactively proposing new approaches that could enhance our solutions. Contribute to the evolution of Engineering Data Quality, bringing innovative ideas and a forward-thinking mindset to continuously improve our modeling and tooling landscape. Nice to Have: Experience with TensorFlow, PyTorch, neural networks, or deep learning applications. Experience with pytest or similar frameworks for data science code quality. Experience with P6 (Primavera), MS Project, or similar project execution systems. Model versioning, experiment tracking (MLflow, Weights & Biases), deployment basics. Familiarity with Azure, AWS, or GCP for data science workflows. Experience with fine-tuning, RAG (Retrieval-Augmented Generation), or agent frameworks. Understanding of data governance principles and responsible AI practices. First-hand experience with SAP, Salesforce, Databricks, or similar ERP/CRM systems from a data consumption perspective. Skills: Strong verbal and written communication skills. Excellent communication and presentation skills. Ability to communicate effectively with stakeholders. Analytical thinking with strong problem-solving abilities. Technical curiosity and willingness to learn new tools and techniques. Collaborative mindset and ability to work in dynamic environments. Self-motivated with a strong sense of accountability. Proactive communication style. Benefits Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.