MastercardLead Data Scientist
Mastercard is hiring a Lead Data Scientist in Toronto, Canada. Level rates it ; you can apply on Mastercard.
Level's read on this job
- On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, TensorFlow and scikit-learn.
- Level classifies it as a lead role.
- Level lists 1,000+ other open roles at Mastercard.
AI in this role
Develop, deploy, and monitor data science solutions involving machine learning, deep learning, and AI techniques.
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Lead Data ScientistOverview:We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.
The Lead Data Scientist is responsible for developing, deploying, and monitoring Data Science solutions that involve Machine Learning, Deep Learning, and other AI techniques.
Responsibilities:
1. Transform business challenges into scientific research problems
2. Develop data-driven solutions to address business challenges using appropriate techniques (AI, ML, DL, etc.)
3. Build and execute various data science projects for product and business use cases across the Ethoca organization (both product and technology).
4. Hands on data extraction, data analysis, data cleaning, preparation, modeling, and evaluation
5. Deploy, document, maintain and monitor the developed solutions
6. Presenting data science results to a variety of audience
7. Protect the developed algorithms and solutions by patenting the technology and consider publishing at academic and industrial research conference.
8. Build prototypes and proof-of-concepts and conduct tool evaluations.
9. Knowledge dissemination by presenting work externally at conferences and Universities.
10. Collaborate with internal teams and across other Mastercard teams
All about You:
1. Substantial experience in developing and deploying Machine learning and Deep learning solutions end-to-end (data exploration to deployment). Will consider even 3+ years if candidate has a Ph.D. from relevant areas.
2. Deep understanding of different Machine learning, Deep learning, and AI algorithms and the math behind it.
3. Strong scientific communication skills.
4. Handling data responsibly by maintaining data governance, privacy, and ring-fencing standards
5. Curious, Critical thinker, good hacking skills and scientific reasoning.
6. Not afraid to ask questions and propose new ideas
7. Master’s in computer science or quantitative field is a must and PhD is good to have.
Skills:
Python, R Programming
ML Frameworks (Scikit learn) and Deep Learning Framework (TensorFlow, PyTorch)
SQL, SAP HANA, Hadoop, Snowflake databases
Linux, Azure environment, Azure Machine Learning, and Databricks
Nice to have:
1. In addition to the above, identify new challenges that can be solved using the data.
2. Generate new innovative ideas that can potentially add value to the organization.Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
Abide by Mastercard’s security policies and practices;
Ensure the confidentiality and integrity of the information being accessed;
Report any suspected information security violation or breach, and
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
Pay Ranges
Toronto, Canada: $127,000 - $203,000 CADHow we rate this
Lead Data Scientist at Mastercard rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical data scientist job
- $165k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where data science was part of your work. What did you do?
- Tell me about a project where python was part of your work. What did you do?
- Tell me about a project where model evaluation was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Machine learning, Deep learning, Data Science, Python, and Model Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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