Principal Data Scientist

Finning International Inc. 1 Permanent The Principal Data Scientist will be focused on solving real world business challenges and optimizing our business performance across a wide range of areas including supply chain, pricing, sales, and marketing, offering advanced analytical solutions for Business Forecasting, Optimization, Pricing, and Reliability/Survival Analysis/Remaining Useful Lifetime modelling, and other predictive and inferential analytics projects as needed. The Principal Data Scientist will rely on their analytical background and passion for Statistics, Modelling, Machine Learning, & Optimization to lead a team to solve problems, develop proof of concepts, validate and verify results, and work with Data Architects and Data Engineers to build end-to-end data processing pipelines.
Reporting to the Director, Equipment Pricing & Data Science, the Principal Data Scientist will work with other business leaders, including Project Managers, Product Managers, Architects, Software and Data Engineers and more as we continue to explore new areas inside and outside of the business.
In this hybrid of a digital and business facing role, excellent communication skills conveyable to all levels of leadership are required as well as the ability to translate complex concepts into more digestible terms. The Principal Data Scientist will work with their director to manage stakeholder expectations during this process and help to evangelize the power of data and data science driven decisions.

As we are extremely interested in these specific domains, please tailor your resume to focus on your projects, experience, and results related to the Data Science domains of Business Forecasting, Optimization, Pricing, and Reliability.

• Collaborate closely with business stakeholders to deeply understand their core problems and requirements, delivering impactful solutions that address their real needs.
• Carry out technical risk analysis and reliability assessments
• Provide recommendations for business plans, programs, strategies, policies, and budgets
• Responsible for the design of algorithms that require different models/methods to be used together
• Working through technical issues, providing technical direction
• Effort estimation, managing stakeholder expectations
• Communication, Presentations

• Develop and implement cutting-edge forecasting, predictive, and inferential solutions tailored to our specific business needs, ensuring they are at the forefront of technological advancements.
• Lead the creation of technically robust and reliable solutions, integrating best practices in software development and data science to ensure their scalability, efficiency, and maintainability.
  Industry research into best practices
• Ongoing discussions with perspective stakeholders into new initiatives
• Pitches, proposals, presentations, prospects
• Provide inputs into DSS strategy/roadmap based on advancing data science practice

• Provide technical mentorship and guidance to a team of Data Scientists and Senior Data Scientists, fostering their professional growth and ensuring excellence in their work.
• Drive a culture of continuous improvement by relentlessly pursuing enhancements and optimizations for our end solutions.
• Regular check-ins, coaching, technical knowledge sharing
• Creating cross-functional opportunities for team members (within team and outside of team) in data science

• Provide inputs into budget/forecasting for the Data Science team/strategy
• Cost controls/monitoring for global data science function

• Accountable for the results of a team that performs complex analyses, including optimization, text analytics, machine learning, social-science modeling, and statistical analysis, parametric and non-parametric statistical models and techniques
• Accountable for communicating analytics model behavior/results to business and driving value out of this
• Accountable for providing the data science inputs on the global strategy & roadmap
• Accountable for Data Science individual contributors to complete project deliverables on time and to acceptable quality standards, including ensuring acceptable transition to sustainment and support of deliverables.

• Capable of designing, building, and implementing complex analyses that span multiple technical areas;
• Capable of communicating the value of data science initiatives to key stakeholders;
• Capable of leading technically senior staff members whose skills can vary the broad span and depths of the entire data science practice
• Comprehensive knowledge of modern data science and product/web analytics tools and techniques, including Python, R, and cloud systems
• Specific knowledge of how to apply data science techniques to achieve enterprise value.

• Advanced quantitative and statistical analysis skills to solve business problems and provide practical business insight using data and a quantitative/scientific approach
• Possess exceptional verbal and written communication skills to relate with and supervise teams, as well as to communicate technical content and analytical insights/complex findings in a clear and concise manner to multiple audiences, including senior management/executive leadership team.

• PhD (preferred) or Master’s degree in Statistics, Mathematics, Econometrics, Computer Science, or another quantitative field from an accredited university.
• About 10 years of post-education experience, demonstrating a significant focus on the relevant areas for Finning: Reliability/Survival Analysis/Remaining Useful Lifetime modelling, Business Forecasting, Pricing, and Optimization.
• Exceptional coding skills in either R or Python, with a track record of developing sophisticated data-driven models and algorithms and using them to drive results.
• Proficiency in utilizing cloud technologies (we use Azure), for distributed computing and efficient processing of large-scale datasets.
• Excellent communication, collaboration, and influencing skills, with a genuine passion for working closely with business stakeholders to drive impactful outcomes.
• Experience leading and managing data science projects, including scoping, planning, and successful execution within defined timelines.
• Familiarity with big data frameworks and technologies (e.g., Hadoop, Spark) for handling and processing large datasets.