Director, Data Science & Engineering, Finance

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $203k - $309k / year

⏰ Full Time

🔴 Lead

📊 Data Scientist

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📋 Description

• Lead a small, high-impact team of a data scientist and a data engineer supporting Finance • Manage, coach, and develop team members through clear expectations, development plans, delegation, and ownership • Set team strategy and priorities across strategic initiatives, recurring operations, model maintenance, knowledge coordination, and technical debt • Provide technical leadership across data science and data engineering, including problem framing, modeling, data architecture, pipelines, testing, monitoring, documentation, and production readiness • Identify and establish standards for AI, machine learning, automation, advanced analytics, experimentation, evaluation, interpretability, human oversight, privacy, and monitoring • Improve financial data quality, lineage, observability, documentation, governance, and reliability • Ensure models and analyses support financial planning, forecasting, driver understanding, and decision-making • Establish operating mechanisms for intake, prioritization, roadmaps, reviews, incident response, documentation, knowledge sharing, and stakeholder communication • Communicate data, model, and engineering implications, recommendations, risks, and trade-offs to technical, non-technical, Finance, and executive audiences • Partner with Finance, Engineering, Data, Product, software engineering, and executive stakeholders • Automate recurring tasks, improve service levels, and increase the team’s strategic contribution

🎯 Requirements

• Bachelor’s degree or above in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative discipline, or equivalent practical experience • Significant experience applying data science, machine learning, analytics, data engineering, or a combination of these disciplines to business problems • Experience directly managing and developing technical professionals, ideally including both data scientists and data engineers or closely adjacent roles • Strong professional foundation in either data science or data engineering, with demonstrated ability to work effectively across the other discipline • Experience leading a portfolio of projects from problem definition through delivery, adoption, measurement, and ongoing operation • Experience with data quality, data pipelines, analytical datasets, model development or maintenance, experimentation or evaluation, and production or operational support • Demonstrated ability to use data and technology to influence business decisions and deliver measurable impact • Strong communication and stakeholder-management skills, including the ability to explain technical concepts, uncertainty, and trade-offs to Finance and executive audiences • Demonstrated growth mindset: actively seeks feedback, learns new technologies and domains, adapts to changing conditions, and creates an environment where the team does the same • Ability to operate with a high degree of autonomy and ownership in an ambiguous, fast-changing environment

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