Quantitative Analyst co‑op/internship focused on data analytics, modeling, and machine learning within BMO’s financial services.
Compensation
$65,000.00 - $140,000.00
Responsibilities
Develop and leverage APIs to extract, integrate, and automate data workflows
Create data visualizations, reports, and interactive dashboards
Apply Bayesian statistics and machine learning techniques to real‑world problems
Design and construct new processes for modeling data
Develop predictive models and machine learning algorithms
Requirements
Master's degree or PhD in Quantitative Finance, Financial Engineering, Statistics, Mathematics, Computer Science, Data Science, Economics, or related quantitative discipline
Nice to have: machine learning in financial services
Nice to have: cloud‑based analytics platforms
Nice to have: advanced quantitative research methods
Own machine learning projects end to end, from understanding the business need and defining the approach through execution, launch, measurement, and iteration.
Locations (2)
Austin, Texas, United States
Seattle, Washington, United States
Compensation
The total cash range for this position in Austin is $112,000.00 to $156,500.00. Employees in this role have the potential to increase their pay up to $179,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.
The total cash range for this position in Seattle is $112,000.00 to $156,500.00. Employees in this role have the potential to increase their pay up to $179,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.
Responsibilities
Own machine learning projects end to end, from understanding the business need and defining the approach through execution, launch, measurement, and iteration.
Develop and improve the Marketplace Health Model system to identify behaviors associated with trust, safety, and marketplace-health risks.
Translate complex marketplace challenges into scalable machine learning and statistical modeling solutions.
Compensation for this role is based upon experience, qualifications, location, and education and typically ranges from $140,000 to $160,000.
Responsibilities
Develop and implement statistical, predictive, optimization, machine learning, and AI solutions
Drive data science projects from discovery to deployment
Define scope, identify risks and dependencies
Build, validate, and evaluate predictive and statistical models
Develop analytical and machine learning solutions with production use in mind
Requirements
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related quantitative field, or equivalent professional experience
Nice to have: Experience with Databricks, Spark/PySpark, MLflow
Nice to have: Familiarity with modern analytics engineering and development practices
Nice to have: Experience building analytical applications or internal data products using tools such as Streamlit
Nice to have: Experience with cloud platforms such as AWS or Azure
You are a passionate software engineer with expertise in developing scalable data pipelines, designing robust analytics platforms, and building infrastructure to support machine learning initiatives.
Locations (4)
San Jose, California, United States
New York, United States
Washington, United States
Illinois, United States
Compensation
The applicable full salary ranges for this position, by specific state, are listed below:
New York City Metro Area:
$125,700.00 - $203,100.00
Non-Metro New York state & Washington state:
$111,700.00 - $180,800.00
* For quota-based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined.
** Employees in Illinois, whether exempt or non-exempt, will participate in a unique time off program to meet local requirements.
Seeking a Lead Data Scientist to provide senior scientific leadership for Data Science and applied-model development within Digital Engineering, Digital & IT.
Locations (2)
Newbury, Berkshire, United Kingdom
Paddington, London, United Kingdom
Responsibilities
Frame customer, commercial and operational challenges as testable Data Science questions, defining outcomes, baselines, assumptions, decision thresholds and measures of value.
Lead the development and validation of predictive, forecasting, propensity, anomaly and fraud, optimisation and causal models for prioritised use cases.
Define appropriate experimental design, sampling approaches, feature strategies, evaluation methods and statistical tests based on the decision being supported and the available data.
Lead modelling, experimentation and scientific validation, ensuring analytical approaches are proportionate to the problem and evidence available.
Set and assure scientific standards for reproducibility, peer review, explainability, fairness, bias, uncertainty and model limitations.
Build clean historical datasets, maintain point-in-time data, run backtests and statistical checks, automate monthly signal production, develop HMM, regime-switching and reinforcement learning models, and build dashboards to track data quality and strategy performance.
Responsibilities
Build clean historical datasets
Maintain point-in-time data
Run backtests and statistical checks
Automate monthly signal production
Develop HMM, regime-switching and reinforcement learning models
Requirements
Master’s degree or PhD in machine learning, computer science, statistics or a related quantitative discipline
Entry-Level Data Scientist focused on graph algorithms and data management
Responsibilities
Design, implement, and optimize graph algorithms and data structures for decision support
Supervise, help create, and implement algorithms for encoding, embedding, clustering, outlier detection, community detection, and dimensionality reduction
Build distributed training pipelines for large graphs
Design and write queries, stored procedures for data loading, migration, and retrieval
Contribute to white papers and technical documentation
Requirements
Bachelor's degree in computer science, mathematics or engineering
Partner closely with product, engineering, operations, trust and safety, and other stakeholders to understand needs and deliver practical solutions.
Stay highly engaged with business outcomes by identifying opportunities where machine learning can create meaningful, measurable impact.
Requirements
Graduate degree in a quantitative field (such as Computer Science, Statistics, Machine Learning, Operations Research, or similar) with focused coursework or research in ML, optimization, or statistical modeling
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