Machine Learning Engineer at Branch International

Department: Engineering
Level: Experienced

Job Overview

• We are looking for talented Machine Learning Engineers to join us and be part of this journey.
• You will work closely with other Engineers, Product Managers, and Data Scientists to develop, improve, and deploy machine learning models and to solve other optimization problems.
• We make extensive use of machine learning in our credit product, where it is used (among other things) for underwriting and loan servicing decisions.
• We are also actively exploring other applications of Machine Learning in some of our newer products, with the ultimate goal of improving the user experience.
• Machine Learning sits at the intersection of a number of different disciplines: Computer Science, Statistics, Operations Research, Data Science, and others.
• At Branch, we fundamentally believe that in order for Machine Learning to be impactful, it needs to be closely embedded into the rest of the product development and software engineering process, which is why we emphasize the importance of software engineering skills and experience for this role.
• As a company, we are passionate about our customers, fearless in the face of barriers, and driven by data.
• As an engineering team, we value bottom-up innovation and decentralized decision-making.
• We believe the best ideas can come from anyone in the company, and we are working hard to create an environment where everyone feels empowered to propose solutions to the challenges we face.
• We are looking for individuals who thrive in a fast-moving, innovative, and customer-focused setting.

Project Examples / Responsibilities

• Credit Decisions - Core to our business is understanding and building signals from unstructured and structured data to identify good borrowers 
• Customer Service - Using machine learning, automate customer service interactions and provide context to our customer service team 
• Fraud Prevention - Identify patterns of fraudulent behaviour and build models to detect and prevent these behaviours 
• Product Growth - Understand user experiences, test ideas, and improve conversion rates through experimentation 
• Paid Growth / Marketing - Use external and internal data sources to measure and optimize marketing spend

Qualifications

• You excel at software engineering and programming in Python and SQL for production systems.
• You have 5+ years of experience working on Machine Learning systems in a production setting.
• You have a diverse range of data skills including experimentation, statistics, and machine learning, and have used these skills to inform business decisions.
• You have a keen eye for detail and a healthy skepticism for data before relying upon it.
• You are highly entrepreneurial.
• You teach yourself new skills.
• You take the initiative to solve problems before they arise.
• You know that startups are a team sport.
• You listen to others, speak your mind, and ask the right questions.
• You are a great collaborator and teacher.
• You are driven by making an impact on customers' lives.

Location:

• This is a remote role.
• The ideal candidate is able to work in the West African Time (WAT) time zone and collaborate with colleagues around the world.

Benefits of Joining

• Mission-driven, fast-paced and entrepreneurial environment
• Competitive salary and equity package
• A collaborative and flat company culture
• Fully paid life and health insurance benefit with Axa Mansard (Platinum plan)
• 28 days of paid vacation days, 30 days sick leave, bereavement leave, and periodic mental health days
• Fully paid parental leave - 6 months maternity leave and 3 months paternity leave
• Pension benefit 
• Monthly WFH stipend alongside a one time home office set-up budget
• Annual professional development budget 
• Discretionary trips to our offices across the globe, with global travel medical insurance (when it’s safe to travel!)
• Weekly team meals and social events (virtual for now!)
• Flexible working hours
• Where we work: Remote first, with the option to work in-person occasionally.