Senior Data Scientist​/Machine Learning Engineer

Overview

Senior Data Scientist / Machine Learning Engineer.

Location:

Manhattan, NY. On-site, Hybrid; 3 days on-site in New York, NY and 2 days remote. Pay: $50 - $58/hr type:
Contractor. Posted: 15 hours ago.

Job Summary:

Our Fortune 500 client is seeking a highly skilled Senior Data Scientist / Machine Learning Engineer to design, build, and maintain advanced analytics and machine learning solutions that drive diagnostic and predictive insights. This role blends hands-on data science, ML engineering, and MLOps, with a strong focus on developing production-ready models, ensuring data quality, and translating complex data into actionable insights for both technical and non-technical stakeholders.

The ideal candidate brings deep experience in Python, SQL, PySpark, Azure Databricks, machine learning, and modern AI techniques, along with the ability to collaborate cross-functionally with Data Engineering, Analytics, and Architecture teams.

• Advanced Analytics & Machine Learning:
  Design, develop, and optimize machine learning models including forecasting, classification, and clustering solutions. Apply data mining and statistical techniques to uncover trends, patterns, and insights from large, complex datasets. Perform feature engineering, model training, validation, and performance tuning. Explore and deploy modern AI, GenAI, and NLP approaches to enhance automation and analytical capabilities. Support A/B testing and experimental design to evaluate model and business performance.
• Data Preparation & Quality:
  Prepare and analyze structured and unstructured data for advanced analytics and modeling. Develop scripts and tools for data cleansing, validation, enrichment, and transformation. Partner with Data Engineering to ensure efficient, reliable data pipelines and scalable data access. Identify data quality issues, perform root cause analysis, and recommend remediation strategies. Troubleshoot and resolve dashboard and reporting data issues to ensure accuracy and consistency.
• MLOps & Engineering:
  Design, deploy, and maintain production-grade machine learning models. Apply MLOps best practices for model versioning, monitoring, retraining, and performance tracking. Write high-quality, maintainable code following software engineering best practices. Leverage cloud-based infrastructure to support scalable analytics and ML workloads.
• Analytics, Insights & Reporting:
  Conduct deep-dive analyses to support diagnostic, predictive, and prescriptive use cases. Communicate complex analytical findings in clear, concise terms to both technical and business audiences. Support the development of dashboards, metrics, and analytical solutions using tools such as Power BI and DAX.
• Cross-Functional Collaboration:
  
  Work closely with architects, engineers, analysts, and business stakeholders to define analytical requirements. Contribute to conceptual data model design, workflow optimization, and analytics standards. Promote best practices in data science, machine learning, analytics, and data governance.

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field.
• 7+ years of experience in data science and machine learning engineering.
• Strong experience with machine learning algorithms, predictive modeling, and data mining.
• Proficiency in Python (required) for data science and ML workloads.
• Strong SQL (required) skills with relational databases.
• Proficiency in PySpark (required) for large-scale data processing.
• Experience with Azure Databricks, Oracle, and modern data science libraries such as scikit-learn, pandas, and Num Py.
• Experience with GenAI and large language models.
• Hands-on experience with MLOps and deploying ML models into production.
• Experience with Natural Language Processing (NLP).
• Minimum 3 years of experience with data visualization tools such as Power BI, including DAX queries and best practices.
• Strong ability to interpret complex datasets and produce actionable insights.
• Excellent communication, analytical, and problem-solving skills.

• Knowledge of statistical methods and experimental design.
• Experience with A/B testing frameworks.
• Familiarity with cloud-native analytics and ML infrastructure.

EEO Statement:
This description is intended to provide a representative overview of the role and responsibilities. It is not an offer of employment and is subject to change. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.