Job Summary

MX is actively seeking a results-driven Director of Data Product Engineering with a proven ability to execute and deliver impactful data products with speed and agility. This role demands a practitioner's mindset, leading a high-performing team of software engineers, data analysts, and data scientists to iteratively build, launch, and scale solutions that will redefine how millions of users interact with and derive value from their financial data. This will be achieved by the Director leading efforts in analyzing, enhancing, and making predictive models from billions of financial transactions, accounts, and user data. This data is core to MX’s mission to empower the world to be financially strong and powers our products that drive growth for our customers. This Director will champion the practical application of cutting-edge machine learning and AI, including pioneering uses of LLMs for predictive data enrichment, transforming complex challenges into market-leading data products. This is a key leadership opportunity for an individual passionate about rapidly engineering and deploying data-driven innovations that create significant business value.

Job Duties

• Leadership and Mentorship: Manage, mentor, and grow a multidisciplinary team of software engineers, data analysts, and PhD-level data scientists, fostering a collaborative, agile, and high-performance engineering culture.

• Data Product Strategy & Execution: Collaborate with product leaders to define and execute the roadmap for data product development, including the application and scaling of machine learning models, from ideation through to production and iteration.

• MLOps Excellence: Champion and implement best practices in MLOps (CI/CD for machine learning, model monitoring, versioning, automated retraining) to ensure the reliability, scalability, and performance of our data products using GCP-based solutions.

• End-to-End Product Delivery: Oversee the entire lifecycle of data products, ensuring robust architecture, high-quality code, and seamless deployment of features that often incorporate advanced machine learning capabilities.

• Cross-Functional Collaboration: Partner closely with product managers, business stakeholders, and other engineering teams to identify opportunities for new data products, define requirements, and deliver impactful, operable, and efficient solutions.

• Technical Guidance & Architecture: Provide technical leadership in software architecture for data-intensive applications, model selection for ML features (including foundation models and LLMs), feature engineering, model evaluation, and cloud-based infrastructure, particularly within the Google Cloud Platform ecosystem.

• Stay Current: Keep abreast of the latest advancements in data engineering, applied machine learning (including developments in LLMs and generative AI), MLOps, and relevant Fintech trends to ensure our data products remain cutting-edge and effective.

• Data Governance and Ethics: Ensure all data products and underlying machine learning initiatives, including those using LLMs, adhere to data privacy regulations, ethical AI principles, and industry best practices.

Job Requirements

• Proven Leadership: 7+ years of experience leading and managing technical teams focused on data engineering, software engineering, and/or machine learning, with at least 3 years in a senior leadership capacity.

• Strong Engineering & MLOps Background: 10+ years of experience in software engineering and/or MLOps with a significant focus on building and operationalizing data-intensive applications and machine learning systems. Demonstrable experience in MLOps principles and tools.

• Applied AI and Machine Learning Acumen: Solid experience in the practical application of machine learning, including selecting, fine-tuning, and deploying models (e.g., predictive analytics, NLP, experience with LLMs highly desirable) as part of larger data products.

• Data Science Understanding: Strong foundational knowledge of core data science concepts, machine learning algorithms, statistical modeling, and evaluation metrics, enabling effective leadership and collaboration with PhD-level data scientists.

• Tech Stack Proficiency:

  • Programming Languages: Python (expert level) and Ruby on Rails is preferred. Experience with other languages like Go is a plus.

  • ML Frameworks & Models: TensorFlow, PyTorch, etc. Experience with frameworks and techniques for fine-tuning and deploying Large Language Models.

  • Cloud Platforms & Services: Extensive experience with Google Cloud Platform (GCP) is highly preferred. Specific expertise in Vertex AI (including its Generative AI capabilities) for model development and deployment, and BigQuery for data warehousing and analysis is essential.

  • Development Tools: Experience with Jupyter Notebooks for data exploration, model prototyping, and analysis.

  • MLOps Tools: Experience with tools like Kubeflow, MLflow, or similar, particularly their integration or equivalents within the GCP ecosystem for managing the lifecycle of diverse model types.
    

• Fintech Domain Knowledge (Preferred): Experience working in the Fintech industry (e.g., payments, lending, trading, risk, compliance) is a significant advantage.

• Excellent Communication: Ability to articulate complex technical concepts related to data products, engineering, and advanced AI/ML applications to both technical and non-technical audiences.

• Problem-Solving Mindset: A pragmatic, product-focused, and results-oriented approach to problem-solving.

• Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field. A PhD is not a requirement; practical engineering leadership and product delivery experience are prioritized.
  

Work Environment

At MX, we utilize a hybrid work model, which allows us to attract top talent and increase impact through collaboration. Our team members enjoy a balance of remote work and in-office days. Travel expectations for remote employees is about 15%, and the company covers travel expenses for remote employees. Local employees will utilize in-office time on a weekly basis Tuesday through Thursday. Both local and remote employees can take advantage of our incredible office space with onsite perks like company-paid meals, onsite massage therapist, golf simulator, and meditation room to name a few.

At MX, we are a high-performance organization that thrives on trust and results. This role is based in Lehi, Utah, with flexibility for both in-office and remote work. We believe in empowering our team members to deliver exceptional outcomes while taking advantage of our incredible office space when it best supports their work. Our Utah office features onsite perks such as company-paid meals, massage therapists, a sports simulator, gym, mother’s lounge, and meditation room and meaningful interactions with amazing people. We encourage team members to come together in the office to collaborate, kick off key projects, or strategize cross-functionally, fostering connection and innovation.

MX is proudly committed to recruiting and retaining a diverse and inclusive workforce. As an Equal Opportunity Employer, we never discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, military or veteran status, status as an individual with a disability, or other applicable legally protected characteristics. We particularly welcome applications from veterans and military spouses. All your information will be kept confidential according to EEO guidelines. You may request reasonable accommodations by sending an email to hr@mx.com.