Manager, Data Science

Accountabilities

• • Translate client and stakeholder needs into clear analytical requirements, objectives, success metrics, and actionable data solutions.
  • Build, maintain, and improve statistical, predictive, sales attribution, conversion, and machine learning models across multiple business areas.
  • Design and implement generative AI solutions, including LLM applications, vector search, embedding-based retrieval, and agentic automation.
  • Support AI-powered workflow agents in production by implementing reliability measures such as event logging, monitoring, and recovery mechanisms.
  • Conduct exploratory data analysis, statistical testing, model comparisons, and documented evaluations to support sound analytical decisions.
  • Develop efficient SQL queries and Python-based data transformation workflows using cloud data platforms, particularly BigQuery.
  • Validate and reconcile pricing, transaction, and performance data against source systems, identifying discrepancies and ensuring reporting accuracy.
  • Build and maintain dashboards, reporting views, and lightweight analytical applications using Tableau, JavaScript, and Google Apps Script.
  • Maintain data pipelines, data architecture, and cloud infrastructure supporting ad hoc analysis and recurring reporting requirements.
  • Develop sales attribution and conversion methodologies, including analysis of channel overlap and contribution.
  • Perform product- and transaction-level analysis, including inventory matching, conversion performance, pricing accuracy, and SLA-related metrics.
  • Conduct code reviews, quality assurance, and audit activities to maintain high standards of analytical and reporting accuracy.
  • Collaborate effectively across distributed onsite and offshore teams, ensuring clear technical handoffs and consistent delivery.
  • Mentor junior team members, share technical knowledge, and contribute to documentation, automation, and standardized QA practices.
  • Communicate analytical findings and complex quantitative concepts clearly to non-technical stakeholders and business leaders.

  Requirements

  • Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, Business Analytics, or a related quantitative discipline.
  • 6–8 years of professional experience in advanced analytics, data science, machine learning, or a related field.
  • Demonstrated experience owning and delivering production data science or analytical models, including client-facing projects.
  • Expert-level proficiency in SQL and Python, with working knowledge of JavaScript and Google Apps Script.
  • Advanced experience with Google Cloud Platform, particularly BigQuery, data architecture, and data warehousing.
  • Strong knowledge of statistical modeling, significance testing, exploratory data analysis, predictive analytics, and experimental methodologies.
  • Practical experience with Tableau and the development of custom dashboards or lightweight web-based analytical tools.
  • Familiarity with LLM-based applications, vector databases, retrieval systems, and agentic AI development practices.
  • Ability to translate ambiguous stakeholder requests into structured analytical requirements, technical solutions, and measurable outcomes.
  • Strong understanding of commercial analytics, including conversion rates, pricing, inventory accuracy, performance measurement, and SLA metrics.
  • Excellent communication skills, with the ability to explain complex technical and quantitative concepts to non-technical audiences.
  • Ability to work effectively in distributed onsite/offshore team structures and manage priorities independently.
  • Strong problem-solving, analytical thinking, documentation, and quality-assurance skills.
  • Experience mentoring colleagues or contributing to team development and knowledge-sharing initiatives.
  • Comfortable balancing hands-on coding and modeling responsibilities with client communication and project leadership.

  Benefits

  • Competitive annual salary range of $94,000–$160,000, depending on experience, knowledge, skills, and other relevant factors.
  • Remote work opportunity for candidates based in Canada.
  • Flexible working approach designed to support collaboration and productivity.
  • Medical, vision, and dental insurance.
  • Life insurance coverage.
  • Short-term and long-term disability insurance.
  • 401(k) retirement plan.
  • Flexible paid time off.
  • At least 15 paid holidays per year.
  • Paid sick and safe leave.
  • Paid parental leave.
  • Opportunities to work on advanced analytics, machine learning, generative AI, and data-driven business solutions.
  • Collaborative environment with exposure to distributed, cross-functional, and international teams.
  • Opportunities for professional growth, technical development, mentoring, and knowledge sharing.
  • Commitment to equal opportunity, inclusion, and reasonable workplace accommodations.