Data Scientist

Description

The position:

As a Data Scientist, you will collaborate with internal teams and our clients to delve into, grasp, and apply data and insights effectively for trust and fraud applications. This role demands a proactive approach to developing, testing, and optimizing machine learning models, ensuring performance excellence and technical accuracy. As a critical team member, you'll be instrumental in creating application specifications and documentation and conveying complex concepts to internal and external stakeholders.

We're looking for a thoughtful, curious, and resourceful Data Scientist to join our growing team. You're comfortable presenting insights to internal and external customers and enjoy continual learning, digging into data, understanding fraud, and applying your programming and machine learning skills. This is a hybrid position with 1-2 days in office and occasional company meetings in person.

What you'll do:

• Work closely with internal and external experts to acquire, comprehend, validate, and utilize data, transforming it into actionable business intelligence.
• Develop, test, and deploy robust machine learning models, ensuring their efficacy and efficiency in real-world applications.
• Actively troubleshoot and refine customer-focused software solutions, maintaining a continuous improvement mindset.
• Identify and resolve application performance issues, focusing on streamlining and optimization.
• Architect, develop and deploy models and algorithms using customer, open source, and proprietary data; assess model quality, and validate and iterate on those models
• Own the process of integrating customer data, analyzing it using our methodology and your data instincts, and make it deliver value to the customer
• Evaluate the effectiveness and accuracy of public and private data sources, choose the right ones for our platform, and help deploy them
• Help design and automate our customer dataset analysis and insights delivery process, to smoothly handle a wider variety and higher velocity of data
• Act as the technical bridge between the customer and the product, making our tools useful, relaying product feedback, and customizing to a client's needs where necessary
• Work with our clients in a consultative capacity, learning about their particular needs and being their advocate both internally and externally.

Requirements

You are:

• A data science professional with at least 6 years of experience and a Bachelor's degree in Computer Science, Mathematics, Statistics, Economics, or a related field; or at least 4 years of experience and a Master's degree in a relevant field; or at least 2 years of experience and a Ph.D. in a relevant field. Strong preference for additional experience in software, R&D, SaaS, or adjacent fields.
• Very proficient with Python. You have experience with creating production-level code and working knowledge of standard ML packages. You have worked on machine learning pipeline code.
• Proven in your experience in applied machine learning, including familiarity with various forms of regression, classification, supervised and unsupervised learning techniques.
• Skilled in handling, cleaning, analyzing, and presenting data.
• Deep in your understanding of statistics and other mindsets for building models from data; strong data acumen in translating business problems into supervised/unsupervised machine learning problems.
• Familiar with cloud technologies (AWS/Google Cloud Platform/Azure).
• Proficient in using Git.
• Comfortable with relational database systems and SQL.
• Excellent in your verbal and written communication skills; comfortable with and effective at delivering presentations.
• Self-driven with the capability to lead projects and perform efficiently independently and as part of a team.
• Authorized to work in the United States.

Extra points:

• Experience with cybersecurity, fraud prevention, or identity resolution solutions.
• Practical experience with any of the following: imbalance data, noisy label handling, semi-supervised methods, self-supervised learning, synthetic data, advanced feature selection, or NLP.

#dice

Working at PIPL
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