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Data ScientistSystematix • New York, New York, United States
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Data Scientist

Systematix
  • US
    New York, New York, United States
  • US
    New York, New York, United States
Postuler Maintenant

À propos

ABOUT THE PROJECT Our client is seeking an experienced Data Scientist to contribute to a cutting-edge data science initiative focused on advanced optimization and predictive modeling. The role supports complex analytical projects that apply mathematical programming and statistical techniques to solve real-world business problems, while also enabling knowledge transfer to internal teams. This is a fully remote engagement offered as a one-year contract, with potential for extension. ABOUT THE RESPONSIBILITIES In this role, you will design, build, and iterate on advanced optimization and predictive models, translating complex business requirements into scalable, solver-ready mathematical formulations. You will work with large datasets and complex problem spaces, applying strong analytical judgment to balance performance, accuracy, and real-world constraints. You will be expected to operate with a high degree of independence, clearly communicate assumptions and trade-offs, and proactively drive work forward while collaborating with technical and non-technical stakeholders. Key responsibilities include: Formulating and implementing optimization models, with a strong focus on mixed-integer linear programming (MILP) and related mathematical programming techniques Translating business objectives and constraints into solver-ready formulations and iterating on models to achieve stable, performant solutions Working hands-on with optimization solvers and APIs in Python (e.g., Gurobi, CPLEX, OR-Tools, PuLP/COIN-OR), including debugging and refining model behavior Developing and applying predictive and statistical models, including Bayesian approaches where appropriate Processing, cleaning, and analyzing large datasets using Python and data-wrangling libraries such as Pandas or Polars Supporting feature engineering and analytical workflows for large-scale optimization or modeling problems Implementing and maintaining data pipelines, including monitoring execution, reviewing logs, and troubleshooting performance issues Applying DevOps practices to support reproducibility, deployment, and maintainability of data science solutions Working with cloud-based data platforms such as Databricks and Azure Blob Storage Clearly communicating assumptions, methodologies, results, and trade-offs to both technical and non-technical audiences Producing clear documentation, model artifacts, and analytical readouts to support transparency and knowledge transfer Proactively identifying risks, surfacing issues early, and seeking input as needed rather than waiting for scheduled check-ins Supporting knowledge transfer and training for internal staff to strengthen organizational data science capabilities REQUIREMENTS Must-have: Demonstrated hands-on experience formulating and implementing optimization models, particularly mixed-integer linear programming (MILP) Strong experience translating business constraints and objectives into solver-ready mathematical formulations Hands-on proficiency with at least one optimization solver or API in Python (e.g., Gurobi, CPLEX, OR-Tools, PuLP/COIN-OR) Ability to debug, iterate, and tune optimization models to achieve stable, performant results Strong Python skills with experience processing and analyzing large datasets using Pandas or Polars Experience working with large-scale data and/or large-scale optimization problems Clear, structured communication skills with the ability to synthesize assumptions, approaches, results, and trade-offs Ability to produce high-quality written artifacts such as documentation, notes, and analytical readouts Self-directed, proactive working style with the ability to operate independently and surface risks early Experience explaining complex analytical concepts to both technical and non-technical audiences Nice-to-have: Experience with Databricks, Azure Blob Storage, or similar cloud-based data platforms Experience implementing DevOps practices within data science or analytics environments Familiarity with Power Apps or Power Automate for workflow automation Experience supporting knowledge transfer, training, or enablement activities Exposure to production monitoring and troubleshooting of data or machine learning pipelines ABOUT THE ROLE Location: Fully remote Duration: 1-year contract (with potential for extension) PAY DISCLOSURE The average hourly pay range for this field is as follows: Junior: 0–5 years of experience — $90–$105/hr CAD Intermediate: 6–9 years of experience — $105–$120/hr CAD Senior: 10+ years of experience — $120–$130/hr CAD Compensation is commensurate with these standards; exceptions may apply based on experience, skills, and market conditions. AI DISCLOSURE We may use artificial intelligence (AI) or other automated tools to support parts of our recruitment process. No automated tools make hiring decisions. APPLY NOW If you are interested in finding out more, please contact us or submit your resume. Or, if you know someone who meets these qualifications, please forward this assignment. ABOUT SYSTEMATIX Systematix is one of Canada’s largest privately-owned National Consulting and Resourcing firms. With offices across North America, we provide the highest-caliber consulting solutions to a diverse client base that includes all levels of government and private industry sectors. Systematix is committed to creating a diverse, inclusive environment and is proud to be an equal opportunity employer. Systematix. Results Focused. People Driven.
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  • New York, New York, United States

Compétences linguistiques

  • English
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