Who you are:

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Lead Data Scientist who will utilize strong problem-solving skills with statistical analysis in order to align data products with Geotab’s organizational goals

What you'll do:

The Lead Data Scientist leads and contributes to medium to large projects; generally these are multi-person projects spanning cross-functional areas both within the data department and across other technical boundaries. You will work to research, experiment, develop, and bring to production data science solutions involving machine learning models, data processing pipelines, novel algorithms, and related topics in the context of fleet management and aggregated telematics solutions. You have the ability to problem solve and create technical solutions for novel business problems, working with non-technical stakeholders as needed to evaluate technical tradeoffs. This role also includes technical mentorship to other Data Scientists.

How you'll make an impact:

• Develop and refine inference and predictive models for the Fleet Safety data product umbrella.
• Collaborate with cross-functional teams including product managers, data analysts, data scientists, developers, user experience analysts and external stakeholders to gather requirements, build technical consensus and drive data products to production.
• Support the definition and shaping of research and development initiatives in areas of subject matter expertise.
• Create algorithms and models required to solve complex business problems.
• Own the design, development, monitoring, and maintenance of scalable models.
• Test the performance of models/data products and make recommendations for improving Geotab’s product suite.
• Take responsibility for the technical output of a project.
• Conduct experiments to identify causality for data anomalies.
• Stay up to date on the latest research in data science, and propose innovative ideas and techniques to advance the field in our business domain.
• Cross team and service boundaries to solve problems.
• Build relationships with stakeholders including customers, product managers, cross-functional teams, and external partners.
• Contribute to defining strategic direction, planning the roadmap, and synthesizing feedback to ensure team success.
• Ensure that knowledge is shared amongst the team and does not position themselves or others as a single point of failure; is skilled at influence without authority, to unblock technical impediments and achieve necessary technical alignments across individuals and teams with minimal escalations. 
• Mentor multiple Data Scientists; work through others; lift the skills and expertise of those around.
• Advise managers with insights and recommendations to improve the technical team; participate in recruiting processes for technical team members.
• Own responsibility for the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards to drive key business decisions.
• Collaborate with business stakeholders to understand their requirements and develop analytical solutions.
• Drive innovation and continuous improvement in data science methodologies and practices within the organization.

What you'll bring to the role:

• Diploma/Degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science or a related field. 
• 10-12 years of Data Science or related experience.
• Thorough knowledge of relevant programming languages (primarily Python, SQL), object-oriented programming principles, Big Data tools and Data Mining/Warehousing Programs (preferably Google Cloud BigQuery).
• Experience with model experimentation, deployment, and monitoring in large-scale cloud-based production systems.
• Experience in working with large amounts of data in SQL.
• Proficient in building models using common Python libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Experience with building, training, and deploying predictive models.
• Experience with AirFlow, Beam, Spark, BigQuery and other cloud based and distributed technologies to scale machine learning models and workflows will be an asset.
• Experience with various supervised and unsupervised machine learning methodologies in areas common to the business such as anomaly detection, time series forecasting, clustering, event prediction, and other areas.
• Relevant industry experience in order to reach the competencies outlined and to demonstrate varied experience in a wide range of problems, pressures, and scenarios on a consistent basis.
• Experience working within a technical or engineering organization; knowledge of the high-technology industry is an asset.