Senior Machine Learning Scientist
TradeRev, Toronto, Ontario, Canada
Revolutionizing Automotive Sales
As a Senior Machine Learning Scientist, you will be responsible for:
- Modeling complex enterprise scenarios, discovering enterprise insights and identifying opportunities in the use of statistical, algorithmic, mining and visualization techniques.
- Communicating results in a way that is customized to the target audience.
- Collaborating with Data Engineers to build the necessary data pipelines
- Kicking-off and monitoring training jobs
- Analyzing the performance metrics of trained models
- Deploying trained models to production.
- Develops experimental design approaches to validate findings or test hypotheses
- Identifies/creates the appropriate algorithm to discover patterns
- Provides ongoing tracking and monitoring of performance of decision systems and statistical models
- Provides business metrics for the overall project to show improvements
- Converses with, writes reports and creates/delivers presentations to colleagues and peer groups in ways that support problem solving and planning. Explains the context of multiple inter-related situations, asks searching, probing questions, and solicits expert advice prior to taking action and making recommendations.
- You're also comfortable exploring the data on your own and have an excellent grasp of statistical methods, i.e., calculation of confidence intervals, different techniques to segment data, hypothesis testing and others.
- An experienced machine learning scientist who worked on highly scalable systems in an agile environment (5+ years)
- PhD or Masters Degree in Computer Science/Engineering, specializing in pattern analysis/recognition, machine learning algorithms or related data-science topics
- Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources is required
- Demonstrated ability to propose solutions to loosely defined business problems by leveraging pattern detection over potentially large datasets.
- As part of professional experience built and deployed customer- facing machine learning systems
- A broad understanding of machine learning techniques from linear regression to neural networks
- You have excellent communication skills and an aptitude for developing relationships at an executive, engineering, and operational levels
- A strong passion for empirical research and for answering hard questions with data is required
- Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner
- Ability to work in a team environment and leading team members.
- Demonstrating sound judgement and decision-making skills
- Strong communication and interpersonal skills
- Deep familiarity with one of AWS, Azure or Google cloud offerings specific to machine learning
- Experience working with scientific libraries such as Pandas, scikit-learn as well as machine learning frameworks such as Keras, TensorFlow, MXNet or others
- Experience working with data processing frameworks such as Apache Spark, Kafka or others
TradeRev is unique because it was created for dealers by a group of people who love cars and are passionate about the industry. With this came a deep understanding of the age-old process of dealing with trade-ins and the challenges that existed within the sales infrastructure. We believe if the industry as a whole advances and evolves, we can live in a world where when it comes to buying a car, everyone comes out a winner. TradeRev Co-Founder Mark Endras recognized these challenges and a truly innovative solution was envisioned: a system that connects dealers all over North America to make moving wholesale inventory quicker, easier and more efficient than ever. The objective was to create technology that powers next-generation automotive marketplaces and, perhaps most ambitious of all, see a world where automotive transactions are fair and easy for everyone. Emboldened by his vision and determined to share his solution, Mark, along with co-founders Wade Chia, Jae Pak and James Tani, worked towards the launch of TradeRev in Toronto in 2011.
Want to learn more about TradeRev? Visit TradeRev's website.
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