Tala is on a mission to unleash the economic power of the Global Majority – the 4 billion people overlooked by existing financial systems. With nearly half a billion dollars raised from equity and debt, we are serving millions of customers across three continents. Tala has been named by the Fortune Impact 20 list, CNBC’s Disruptor 50 five years in a row, CNBC’s World's Top Fintech Company, Forbes’ Fintech 50 list for eight years running, and Chief's The New Era of Leadership Award. We are expanding across product offerings, countries and crypto and are looking for people who have an entrepreneurial spirit and are passionate about our mission.

By creating a unique platform that enables lending and other financial services around the globe, people in emerging markets are able to start and expand small businesses, manage day-to-day needs, and pursue their financial goals with confidence. Currently, over nine million people across Kenya, the Philippines, Mexico, and India have used Tala products. Due to our global team, we have a remote-first approach, and also have offices in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India.

Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you!

We are seeking a Senior Data Scientist – Causal Inference to tackle one of the world’s most pressing issues: financial inclusion. In this role, you will lead high-impact data science initiatives aimed at uncovering cause-and-effect relationships. Your efforts will drive strategic decision-making, optimize credit products, enhance customer experiences, and improve financial operations.

You will manage the entire modeling process, from defining problems and developing causal models to deployment, evaluation, and continuous monitoring. Your expertise in causal inference, experimentation, and collaboration with stakeholders will be crucial in ensuring that data-driven insights lead to measurable business impact.

As a customer-focused leader, you will independently drive data science initiatives that align with Tala’s mission of unleashing the economic power of the global majority. Your work will directly contribute to expanding financial access, improving credit outcomes, and shaping the future of inclusive finance.

What You'll Do

• Embed with business stakeholders to identify, test, and implement data science solutions with a particular focus on econometric models, experiments, and frameworks to assess challenging causal problems such as lifetime value or retention.
• Develop a roadmap in collaboration with stakeholders to organize and prioritize initiatives.
• Own full-cycle machine learning products from ideation and training to deployment into our production environment, including real-time inference and after-sale service. Obsess over machine learning products, deploying and iterating rapidly when necessary.
• Propose, design, and analyze large-scale online experiments and support business stakeholders with experimental design best practices.
• Become a multiplier for the data science team by sharing work, providing mentorship to junior team members, providing technical feedback, and developing work into shared team tooling.
• Be a thought partner for the business, leverage data insights and strong communication skills to influence company strategy. Develop roadmaps in collaboration with stakeholders to organize and prioritize initiatives.

What You'll Need

• 4+ years of experience in a data science role or equivalent position.
• 2+ years in a senior/lead position leading the execution of end-to-end machine learning solutions for business stakeholders.
• Fluency with Python and packages related to machine learning.
• Comfort with SQL.
• Experience with the full modeling cycle, from scoping and development through deployment, after-sale, and monitoring. Demonstrated delivery of business value in previous roles. 
• Competency with statistics. Knowledge of experimental design and analysis. Previous experience with causal modeling techniques.
• Strong collaboration and communication skills. Ability to tell stories through data and effective visualization. 
• Ability to thrive in, at times, ambiguous and complex environments.
• Desire to champion simple, robust systems and to deploy and iterate as needed.