Mission: Helping our customers run at their best.
Vision: At SAP, our purpose is to help the world run better and improve people"s lives. Our promise is to innovate to help our customers run at their best. SAP is committed to helping every customer become a best-run business. We engineer solutions to fuel innovation, foster equality, and spread opportunity across borders and cultures. Together, with our customers and partners, we can transform industries, grow economies, lift up societies, and sustain our environment.
Build bridges, not silos: We put egos aside and work as a team towards a common vision. The competition is outside, not inside.
Embrace differences: We are a diverse and global team. All of us have unique skills and experiences that create value for our customers.
Keep promises: We have a long history of solving complex business challenges. That"s why our customers trust us. We work as one team to constantly earn this trust, delivering the best outcome in the simplest way possible.
Stay curious: We never stop pushing boundaries of what our solutions can do for people and for the world. We are always improving and adapting to stay ahead.
Tell it like it is: We build trust by being honest and authentic. We are professional and constructive and we create platforms where people can openly exchange ideas.
Basics on machine learning like overfitting, design a system for the use case.
Current knowledge of various algorithms.
How would you design a recommendation system for Amazon customers, considering that a single customer may use many devices to logon to a single account?
Techniques for handling missing data.
Evaluation metrics for different uses cases (regression, RSME, and also classification problems).
How would you deal with an unbalanced dataset?
Dimension reduction, recursion, dynamic programming, and big O notation.
Name a project you"ve completed before that other people said was not possible, explain how you approached the project in a constructive manner.
Cross-lingual word vectors, policy gradients, and how to deal with the lack of training data.
Stage 1: Phone screen with recruiter
Stage 2: Online machine learning challenge
Stage 3: Technical phone interview with 2 experts of the team Questions are about previous ML experiences, with scenarios about typical ML problem and questions for the candidate on how he/she would approach it ("what data would you ask for", "what model would you build", etc.), detailed questions about deep learning, e.g. properties of different activation functions, advantages/disadvantages of several methods, etc.
Pathrise is a career accelerator that helps people land their dream jobs. We regularly place our fellows at top companies like Apple, Amazon, and Meta. Our mentors have experience at companies like Apple, giving fellows the inside scoop on interview and company culture in 1-on-1 sessions.
We can’t guarantee you a job at a specific company like Apple. But we do guarantee you a great job–if you don’t accept an offer in 1 year, you pay nothing. Our income share agreement means you only pay with a percentage of your income at your new role.
Mentors work with fellows at every stage in search, helping them build the skills necessary to be the best candidate possible. Fellows in Pathrise usually see a 2-4x increase in application response rates, 1.5-3x increase in interview scores, and 10-20% increase in salary through negotiation.