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Data Scientist, Team Lead

Technology is reinventing higher education and at OCAS we play a vital role in helping shape this evolving landscape. We deliver dependable technology systems and business services to support our industry partners and create new pathways for learners exploring and applying to Ontario’s public colleges.

 

Recently named a Waterloo Area Top Employer for the ninth consecutive year, we’re looking for innovators who can bring their expertise and passion to our growing team.



This position is a parental leave coverage that will last approximately one year.

The Applied Research team at OCAS uses data – sourced from our in-house Data Warehouse and college system partnerships – to generate insights and surface key findings for a wide range of topics related to the post-secondary ecosystem.

As a Data Scientist at OCAS, you’ll play an important role in leading investigations into available data using more advanced and innovative techniques to generate new findings. You’ll focus on determining appropriate data mining techniques, conducting statistical analysis, modelling relationships between variables, and building high-quality prediction systems.

As a Team Lead at OCAS, you’ll mentor a team of data analysts, data scientists, and researchers by setting team and individual goals that drive high-quality research and insights. You’ll assist the team in validating data analyses to ensure accurate research and reporting are delivered from the team.

As a member of the OCAS team, you’ll work with remarkable colleagues who support each other in achieving high performance.

In this role, you will:

  • Lead and mentor a team of data analysts, data scientists, and researchers
  • Work with stakeholders to understand their data needs and build predictive models and machine learning algorithms
  • Identify alternate options for generating insights beyond basic statistics, making recommendations to teams across OCAS
  • Generate information and insights by leveraging large sets of structured and unstructured data to identify trends and patterns
  • Create visualizations of data and prepare reports for executives, project teams, and external stakeholders
  • Process, clean, validate, analyze, and interpret complex data sets
  • Communicate complex results and methodologies to technical and non-technical stakeholders
  • Identify relevant data sources to meet business needs

 

You should have:

  • Degree in Computer Science, Math, or other related areas
  • At least five years of experience as a Data Scientist or similar role using a wide range of research methods, including qualitative and quantitative data processing and statistical testing
  • Proven leadership abilities
  • Experience with regression analysis, including linear regression, logistic regression, multivariate regression, and time-series analysis (e.g., ARIMA, seasonal decomposition, exponential smoothing)
  • Experience with predictive modelling (e.g., random forests, support vector machines)
  • Familiarity with validation techniques, such as cross-validation, confusion matrices, ROC curves, and precision-recall analysis
  • Proficiency in statistical programming languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets
  • Experience with data visualization using python libraries
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, K-means, artificial neural networks, etc.) and their real-world advantages/drawbacks
  • Strong problem-solving skills
  • Excellent communication and collaboration skills

 

Position Reports to:  Senior Director, Product & Applied Research



OCAS is committed to fostering a diverse and inclusive workplace. We welcome and encourage applications from diverse candidates, including people with disabilities.  Accommodations are available on request for candidates taking part in all aspects of the selection process. While we thank all respondents for their interest, only those candidates being invited to interview for this position will be contacted.