We are seeking a highly analytical and results-driven team member to join our growing data science team in the financial services sector. You will play a key role in developing and implementing predictive machine learning /AI models, leading data-driven decision-making processes, managing data science projects from conception to deployment, and extracting insights that drive product development, risk mitigation, and customer strategy. This role requires a strong foundation in statistics, machine learning, and financial domain knowledge.
What You’ll Do:
- Build and deploy ML/AI models to solve business problems in areas like prospect targeting, credit risk, fraud detection, pricing, verification and loss forecasting.
- Analyze large, structured and unstructured datasets using SQL, Python or similar tools.
- Evaluate existing customer data, new third party or alternative data to enhance the feature list and work with data engineering and analytics teams to build scalable and durable feature engineering process.
- Stay up to date with the latest trends and technologies in data science and fintech, actively research new tools and techniques available for model development.
- Collaborate with cross-functional teams including risk, marketing, product, and engineering to define data-driven strategies.
What We Look For:
- Advanced Degree (MS/PhD) in Data Science, Engineering, Statistics, Mathematics, Computer Science, or a related quantitative discipline.
- 5-10 years of hands-on experience in a data science or analytics role, preferably in financial services.
- Strong proficiency in Python (Pandas, Numpy, Scikit-learn) and SQL.
- Ability to write documentation and present analysis to people with different levels of expertise (e.g., technical staff, business leads, etc.).
- Detail oriented and strong analytical skill set.
- Proactive, driven, and ability to work in a fast-paced environment.
Nice to Have:
- Experience in credit risk modeling
- Design and maintain end-to-end CICD pipelines (using Kubernetes or GitHub action or other tools) including continuous integration, deployment, and monitoring. Automate model training, evaluation, and deployment workflows
- Lead end-to-end machine learning and data science product from conception to development
- Experience working with cloud platforms (AWS, GCP, or Azure).
- Knowledge of big data technologies like Spark or Hadoop.
- Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value.
What We Offer You:
- Competitive salary and stock option plan
- 100% paid coverage of medical, dental and vision insurance
- Competitive 401(k) and RRSP program
- Flexible PTO
- Opportunities for professional growth and development
- Paid parental leave
- Health & wellness initiatives
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Upgrade does not accept unsolicited resumes from staffing agencies, search firms, or any third parties. Any resume submitted to any employee of Upgrade without a prior written agreement in place will be considered the property of Upgrade, and Upgrade will not be obligated to pay any referral or placement fee. Agencies must obtain advance written approval from Upgrade’s Talent Acquisition department to submit resumes and only in conjunction with a valid, fully executed agreement.