Search for Machine Learning Engineer roles in Scottsdale, AZ, specifically in the fintech industry, for full-time positions, with a Master's degree or PhD, and requiring experience in fraud prevention.

Okay, I've searched for Machine Learning Engineer roles in Scottsdale, AZ within the fintech industry for full-time positions, requiring a Master's degree or PhD, and specifically looking for experience in fraud prevention. I have found 27 positions for you.

Machine Learning Engineer

Scottsdale, AZ

fintech

Full-time

Master's degree

Can you help me to analyze the job description and company culture?

Sure. Here's the analysis:

Job Analysis:

The Machine Learning Engineer role at PayPal is fundamentally about building advanced fraud prediction models to enhance the security and integrity of financial transactions across the platform. This position requires the development and integration of decision models that target various aspects of fraud prevention, including identity verification, onboarding security, and scam detection. The complexity of this role lies in the candidate's ability to leverage anomaly detection, supervised learning techniques, and continual learning methods to keep up with evolving fraud tactics. Successful candidates will not only have a strong background in data science and machine learning but must also be adept at collaborating with cross-functional teams to translate technical implementations into operational systems. The challenges posed will require innovative problem-solving to navigate ambiguous data scenarios and align fraud detection methods with broader business objectives. Within the first 12 months, success will likely be measured by the effectiveness of models deployed, the adaptability to new fraud patterns, and the promotion of a data-driven culture within the team and organization.

Company Analysis:

PayPal occupies a critical role in the global digital payments industry, striving to create innovative and secure financial solutions for both consumers and businesses. As a leader in this sector for over 25 years, the company is characterized by its commitment to inclusivity, innovation, collaboration, and wellness, suggesting a forward-thinking and employee-centric culture. This emphasis on values indicates that the work environment may prioritize collaboration and encourage initiative among its employees. The Machine Learning Engineer position will likely serve as a bridge between the technical realm and real-world applications, giving this role significant visibility and influence within the organization. Given the focus on fraud prevention, this position aligns with PayPal's larger goal of maintaining trust and security in its services, which is essential to sustaining customer loyalty and expanding market reach. Overall, the company’s trajectory emphasizes growth and adaptability, casting the successful candidate as a key player in driving rapid evolution in fraud detection methodologies within a challenging and dynamic marketplace.

Mock up some interview questions based on the job description.

Absolutely. Here are some mock interview questions that could come up:

Tell me about a time when you had to develop a machine learning model under tight deadlines. What approach did you take?
Describe a situation where you identified an anomaly in data. How did you determine it was an anomaly and what steps did you take next?
Can you provide an example of how you successfully collaborated with cross-functional teams to implement a technical solution? What challenges did you face?
Explain a time you had to adapt a predictive model based on new data or fraud tactics. How did you ensure its effectiveness?
What methodologies do you leverage when developing preventive models for fraud detection? Can you walk me through a specific example?
How would you approach the implementation of a supervised learning technique to build models for identity verification?
Imagine you are working with conflicting data from different sources. How would you resolve this issue to maintain integrity in your models?
If you were tasked with developing a new fraud detection model, what initial steps would you take to ensure its success?
Why do you want to work as a Machine Learning Engineer at PayPal, and how does this role align with your long-term career goals?
How do you see your work contributing to PayPal's mission of creating an equitable global economy?
If hired, what would be your priorities in your first 90 days in this position?
What is your perspective on the importance of a data-driven culture, and how would you advocate for it in your role?
What questions do you have about the specific challenges faced by PayPal in fraud detection, and how does this team tackle them?

Job Details

Overview Company

Machine Learning Engineer

$111.5k/y-$191.95k/y

PayPal

9 days ago

Highlight

HybridFull-timeMid

Summary

PayPal is seeking a Machine Learning Engineer to join their team in Scottsdale, AZ. The role focuses on building advanced fraud prediction models to enhance fraud prevention.

About the Role

As a Machine Learning Engineer, you will design and implement core decision models for identity, onboarding, authentication, abuse, scam, and product-specific models. You will develop and refine algorithms for anomaly detection, apply supervised learning techniques, and utilize continual learning methods to improve model performance. Collaboration with cross-functional teams to integrate fraud prediction models and conducting experiments to drive innovation are key responsibilities.

About You

Required:

Master's degree or PhD in Computer Science, Statistics, Data Science, Machine Learning, Artificial Intelligence, or a related quantitative field (STEM).
Relevant experience within Data Science, ML Engineering, or AI Research roles, with demonstrated expertise in building and deploying real-world predictive models.
Strong understanding of anomaly detection, supervised learning techniques, and experiential learning methods.
Strong interpersonal, written, and verbal communication skills.

Preferred:

Familiarity with decision models for identity and authentication.
Experience in fraud prevention and detection.
Experience driving data instrumentation for experimentation and large-scale data collection.
Familiarity with building systems that incorporate real-time feedback and continuous learning.
Knowledge of reinforcement learning, contextual bandits, sequence models, optimization, or graph mining.

Benefits

Flexible work environment.
Employee shares options.
Health and life insurance.
Annual performance bonus.
Equity.
Comprehensive benefits package including medical, dental, and vision coverage.

PayPal

We're championing possibilities for all by making money fast, easy, and more enjoyable. Our hope is unlock opportunities for people in their everyday lives and empower the millions of people and businesses around the world who trust, rely, and use PayPal every day.

For support, visit the PayPal Help Center. https://payp.al/help

For employment opportunities, check out our job openings in the 'Jobs' tab. We're an equal opportunity employer that welcomes diversity, and offer generous benefits to help you thrive at work and in your free time.

Scottsdale, AZ