Machine Learning Engineer

Houston Texans; Toronto; On-site; CAD 75,000 - 110,000; Full time

Job summary

Join a leading company in sports betting as a Machine Learning Engineer. You will focus on developing predictive models and tools for analysis in a dynamic environment. Ideal candidates will have a strong background in machine learning, sports analytics, and proficiency in R, contributing to innovative projects that redefine sports betting.

Benefits

Mentorship opportunities

Collaborative work environment

Continuous learning and growth

Qualifications

• Experience with statistical learning techniques and data analysis.
• Proficiency in R and modern R packages.
• Demonstrated ability to work with large datasets.

Responsibilities

• Build and iterate on predictive models in production environments.
• Develop tools for model evaluation and performance analysis.
• Analyze model performance against betting clientele.

Skills

Statistical analysis

Machine learning

Data visualization

Algorithm development

Education

Degree in Computer Science

PhD in Machine Learning

Tools

R

SQL

ggplot2

Shiny

Rcpp

dplyr

Job description

About us:

Welcome to Pinnacle, the ultimate destination for sports enthusiasts seeking an exhilarating sportsbook and gaming experience! Established in 1998, we have solidified our position as one of the globe's foremost licensed online gaming companies. With our cutting-edge offerings, we guarantee an electrifying experience that will keep you on the edge of your seat.

Pinnacle invites you to join our team and become an instrumental figure in the exciting realm of sports betting. Our vibrant team is fueled by passion and driven by innovation, working together to redefine the landscape of sports betting and gaming. Together, we constantly strive to surpass limitations and deliver unparalleled experiences to sports enthusiasts worldwide. Prepare yourself for a thrilling journey and discover sports in an entirely new dimension with Pinnacle!

Job Purpose:

We are seeking a Machine Learning Engineer for a role encompassing sports modelling, algorithm development, and both building and utilizing tools to support model evaluation and performance analysis. The ideal candidate will have experience working with traditional statistical learning techniques, probabilistic graphical models, and a deep understanding of data analysis and visualization. Proficiency in R is strongly preferred, as is a demonstrated ability to work with large datasets, develop intuitive visualization tools, and deliver insights to inform decision-making. A passion for sports, sports analytics, sports betting, and fantasy sports is also beneficial.

The ideal candidate should have:

A degree in Computer Science, Statistics, Mathematics, or a related field. Advanced degrees (MS/PhD) specializing in Machine Learning or Statistics are highly desirable.Experience with statistical computing in R, with proficiency in modern R packages and technologies such as data.table, dplyr, tidyr is preferred.Strong experience building and utilizing existing tools for model evaluation and performance analysis, ideally in R utilizing visualization tools such as ggplot2, shiny, and plotly.Deep knowledge of traditional machine learning techniques, such as linear regression, generalized linear models (GLMs), and generalized additive models (GAMs).Expertise in probabilistic graphical models, such as directed acyclic graphs (DAGs) and Markov chains, is a strong plus.Demonstrated ability to work with large datasets efficiently and develop optimized code, with tools such as Rcpp, microbenchmark, and profviz, is a big plus.Experience working with relational databases such as SQL server, PostgreSQL or Google BigQuery.A passion for sports, sports analytics, sports betting, or fantasy sports is highly beneficial.

Working Environment: We foster a collaborative environment that emphasizes skill development, mentorship, and tackling a combination of theoretical and real-world problems in sports analytics and betting. With opportunities for continuous learning and growth, team members expand their technical expertise, gain exposure to new technologies, and deliver impactful solutions in a fast-paced, dynamic industry, supported by open communication and mutual respect.

Core Responsibilities: This is a key role within the team, focusing primarily on building and iterating on predictive models, and developing tools to support model evaluation and performance analysis. These models are released to real production environments, analyzed by expert traders, and eventually form the basis of the initial layer of our odds engine. The performance of these models are analyzed against our sharp betting clientele and constantly iterated upon based on the changing dynamics of market trends, data feed quality, sport-specific gameplay, and rule changes.

Technical Expertise: A strong background in statistics, machine learning, and algorithm development is essential. Candidates will ideally have advanced proficiency in R, with experience coding for production environments. However, for those who are less experienced with R directly, we offer a highly supportive internal ecosystem with dedicated guidance to help develop R skills.

Development Expertise: Understanding of software principles and functional coding principles are necessary. Experience deploying models to production in real world applications with feedback from senior teammates and key stakeholders is a strong plus. Experience with code optimization, Rcpp/C++, and R package development are highly valued. Experience using tools like GIT, JIRA, along with participating in peer reviews to ensure high quality, maintainable code in a collaborative development environment is highly desired.

Data and Evaluation Focus: Demonstrated ability to work with large datasets, optimize code for performance, and deliver insights through well-designed tools is critical. Experience with Shiny for building tools and ggplot2 or similar libraries for data visualization is highly valued.

Soft Skills: Strong communication skills (both verbal and written) are required to convey complex information effectively. The ideal candidate will also have strong analytical, conceptual, and problem-solving abilities, with attention to detail.

Domain Knowledge: A thorough understanding and passion for sports, sports analytics, and sports betting markets is highly desirable. Experience working with sports betting markets, financial markets, or similar applications is a plus.

Bonus Skills: Experience with Bayesian techniques and their implementation to solve practical problems with software like STAN or NIMBLE, is a plus, though not required. Familiarity with Rcpp, C++, and tools like profvis, microbenchmark, parallel is a strong plus