Data Science and Analytics Consultant

About the role

The Data Science and Analytics team builds advanced analytics and AI solutions that turn business strategy into scalable, data-driven solutions, working closely with Product, Engineering and other stakeholders. This Consultant role reports to the Director, Data Science and Analytics and is remote, with occasional in-person attendance at work-related events at management's discretion.

Key responsibilities

• Own end-to-end delivery of AI, data science and analytics projects, from problem definition through deployment and continuous improvement, managing priorities, quality, timelines and business impact.
• Design, build and maintain scalable data pipelines, machine learning models and analytical solutions.
• Lead analytics engagements using AI and descriptive, predictive and prescriptive techniques across diverse use cases.
• Translate business objectives into data-driven roadmaps, define success metrics and show the value of analytical solutions.
• Collaborate with Product, Engineering and business stakeholders on priorities, dependencies and risks.
• Communicate insights, risks and recommendations through executive-ready presentations, visualizations and data storytelling.
• Improve models and workflows through automation, optimization, experimentation and emerging technologies.
• Mentor junior team members through code reviews, technical guidance and knowledge sharing.

Required profile

• Master's degree with 3+ years, or Bachelor's degree with 5+ years, in a quantitative field.
• Experience developing and deploying machine learning models across the full lifecycle, and designing AI solutions with measurable outcomes.
• Strong analytical and problem-solving skills, with experience managing analytical projects independently and communicating with technical and non-technical stakeholders.

Required skills

• Proficiency in Python or R for statistical analysis, modeling and machine learning.
• Advanced SQL for data preparation, analysis and validation.
• Large-scale data processing frameworks such as Apache Spark or Apache Hive.
• Scalable machine learning solutions, data pipelines and production-ready workflows.
• Working knowledge of data visualization, storytelling and cloud or big data ecosystems.
• Preferred: analytics in fraud, identity, credit risk or financial services; ETL frameworks; client-facing analytics engagements.