All mentors

Mahsa Ebrahimian

Head of AI Compliance/Fraud

Toronto, Canada (-05:00 UTC)from Toronto, Canada
Usually responds in 19 hours
Free
Price per hour
30 min
Time Blocks Available
5.00
3 reviews / 3 sessions
Tue
13
Next availability

Bio

I am a senior AI and financial crime leader with deep expertise in building and governing machine-learning systems for mission-critical environments. I currently lead AI-driven compliance and financial crime data science, where I oversee design, deployment, and governance of advanced fraud and AML models at scale. With a strong background in applied machine learning, anomaly detection, and graph analytics, I speciliaze in translating business expectations into practical, production-ready AI solutions. I enable organizations to evolve from legacy decision paradigms to explainable, scalable AI decision intelligence. Over the past several years, I’ve worked at the intersection of AI, fintech, and risk—designing fraud detection systems, leading cross-functional teams, and driving the shift toward data-driven, intelligent decisioning. My work focuses not only on model performance, but on real-world impact—balancing fraud prevention, customer experience, and regulatory requirements to protect global financial ecosystems. What sets me apart is my end-to-end ownership: from defining strategy and KPIs with business stakeholders, to building and deploying models, to navigating the complexities of compliance, operations, and production systems. I partner closely with business and technical leaders to translate strategic initiatives into actionable AI roadmaps, align cross-functional teams across engineering, MLOps, IT, and data governance, and ensure AI solutions move beyond experimentation into scalable, production-grade systems. My focus is on building AI that delivers measurable value—operationalized, reliable, and designed to scale. ** How I Can Help You: - I support professionals and teams looking to build impactful, real-world AI systems—especially in complex, regulated environments. I can help you: - Navigate AI adoption — move from ideas to practical implementation, aligning AI initiatives with business strategy and organizational readiness - Define and measure impact — design meaningful KPIs and connect ML outcomes to real business value, using metrics to guide roadmap and decision-making Productionize ML systems — take models from experimentation to reliable, scalable production with the right architecture, processes, and trade-offs - Drive cross-functional alignment — effectively collaborate with engineering, MLOps, data governance, and business stakeholders to deliver end-to-end solutions - Communicate and explain AI — translate complex models into clear, actionable insights for non-technical stakeholders, including risk and compliance teams - Apply AI in FinCrime contexts — design responsible and effective solutions for fraud detection, AML, and compliance use cases - Implement best practices — improve model monitoring, reduce false positives, and balance performance with customer experience and regulatory expectations

Expertise

  • Artificial intelligence

    I build scalable, explainable AI for fintech and compliance, leading end-to-end ML from strategy to production with real business impact.

  • Data science

    With over 8 years of experience building machine learning models, I can : Advice on applying machine learning on business problems, help data scientists and startups create a clear roadmap for taking models into production, and coach them on explaining model decisions to non-technical stakeholders.

  • Leadership

    I lead cross-functional AI teams, align strategy with execution, and drive high-impact solutions while mentoring talent and influencing stakeholders.

  • Productized services

    Building AI solution or machine learning models for the purpose of productionalization, for scaleability and reuse.

Experience

  • Women in Big Data

    Mentor for Aspiring Data Scientists

    Mentoring aspiring data scientists as they transition into the field, guiding them through the early stages of their career journey. My support includes: Resume Review & Positioning – helping them highlight technical skills and frame past experience to align with data science roles. Narrative Coaching – rethinking how they present their knowledge, projects, and career story to resonate with hiring managers. Career Insights – sharing what the day-to-day role of a data scientist looks like in a corporate environment, including collaboration, problem-solving, and impact on business decisions.

  • Xe.com

    Manager Data Science FinCrime

    - Lead AI-driven compliance and financial crime data science, overseeing the design, deployment, and governance of fraud and AML models at scale - Build and govern machine learning systems in mission-critical environments with a focus on reliability, explainability, and impact - Translate business needs into production-ready AI solutions using applied ML, anomaly detection, and graph analytics - Drive the transition from legacy decision systems to scalable, data-driven, AI-powered decisioning - Own end-to-end AI lifecycle: strategy definition, KPI design, model development, deployment, and production operations - Partner with business and technical stakeholders to define AI roadmaps and align cross-functional teams - Ensure AI solutions move beyond experimentation into scalable, production-grade systems delivering measurable business value - Balance fraud prevention, customer experience, and regulatory requirements to protect global financial ecosystems

  • Xe.com

    Senior Data Scientist and FinCrime team lead

    - Leading a cross border team of data scientists to continuously improve the Fraud Prediction Model in batch and real-time - Designing the feedback loop process and monitoring the performance of the ML model in production - Communicating with Mlops , Data Engineers and the business team to translate business requirements to machine learning solutions - Assisting the compliance team in scaling and optimizing their operations during the expansion of the fraud detection system - Shaping the data science team's roadmap to align with business needs and addressing technical dependencies to the engineering team

  • Xe.com

    Data Scientist

    - Building machine learning models for fraud prediction - Working with ML and data engineers to bring the machine learning model into production - Developing supervised and unsupervised models to detect fraudulent cases in Fintech

  • Ryerson University

    Machine Learning Researcher

    Adviser: Dr. Rasha Kashef Conducting research in Recommendation Systems in E-commerce (collaborative-filtering, content-based), with machine learning approaches (supervised and unsupervised methods , deep learning).

  • MTN

    Data Scientist

    working in a Telecom industry, every day we faced new challenges to address business and market's requirement. In order to provide data driven solutions, our data science team worked on comprehensive mathematical models to predict churn, engagement and other customer's behavior. I was mainly in charge of : Predictive modeling of customer behavior and market analysis, such as subscriber’s usage and churn prediction with programming in python and Oracle database. Aggregating Data, Designing Data ware house and ETL process on high volume and velocity oracle database. Designing Qlikview Dashboard for business people and providing real time reports from billions of records each day. Recipient of questions from company’s CFOs and marketing team managers, understanding the questions and determining the data needed to find the solutions for marketing strategies Doing ad-hoc analysis and Presenting analysis conclusions to clients in a clear, understandable and valuable way Enhancing data collection procedures to include relevant information, processing, cleansing and verifying the integrity of data used for analysis Defining segmentation groupings and building propensity models that predict churn, engagement and other user behaviors

  • MTN

    Data Analyst

    -Automated repetitive tasks through the use of complicated scripts, ETL and Reporting tools. -Evaluated huge data sets for accuracy and quality, such as Data revenue of 80 M subscribers and their usage and buying behavior. -designed and implemented a model to offer customized data offers to subscribers, which resulted in +3% revenue increase. -created a comprehensive NPV model for measuring sites' profitability as well as suggesting high potential locations for rolling out new sites, which resulted in 25% improvement in Capex optimization. -Created Data warehouse of subscriber level data, using SSIS,SQL, Toad Oracle and python.

  • همكاران سيستم | System Group

    Business Analyst

    worked with development team and business units, responsible for system requirement analyst Collected, analyzed, and verified business requirements with users and translated them to system requirements and solutions IT deliveries and domain experience involves :knowledge management , sales and marketing system, Customer support services such as SCSM, customer portal application,voice of customer , strategic planing Effective in working independently and collaboratively in teams Meeting end-users and system owners for requirement gathering and implementation design Use Cases Provide support to existing customers with SQL Contributed to Sprint planning sessions as Product Owner

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