ML - Data Engineer
August 21
Description
• Analyze large-scale structured and unstructured datasets using analytical, statistical, machine learning, or deep learning techniques to address a wide range of complex issues. • Collaborate with stakeholders from various departments to comprehend their business requirements and obstacles, design and develop analytics solutions to achieve business goals, and facilitate decision-making. • Partner with cross-functional teams to provide strategies based on data-driven insights across product, marketing, compliance, and other areas. • Identify, understand, and evaluate external/internal opportunities to enhance our products and services. • Determine and assess the success of product initiatives through goal setting, forecasting, and monitoring of key product metrics. • Create data models, data automation systems, performance metrics, and reporting frameworks, and monitor impact over time. • Execute Machine Learning projects from inception to completion. This encompasses understanding the business need, planning the project, collecting & exploring data, developing & validating models, and implementing ML models to generate business value. • Present results and business impacts of insight initiatives to stakeholders within and outside of the organization.
Requirements
• Adept at dissecting vague and high-level problems and coming up with solutions. • Skilled in data extraction, analysis, and/or modeling. • Capable of working autonomously, with minimal supervision, in a dynamic environment. • Self-motivated, creative, team-oriented, with strong communication and presentation abilities, able to bridge business and technical audiences. • An advocate for documentation, transparent communication, and knowledge sharing. • You have more than 5 years of professional experience with analysis tools and recommendation systems. • Extensive experience in SQL. • Hands-on experience with Looker Studio or similar tools. • Solid experience in Python and data analysis libraries such as pandas, numpy, matplotlib, scikit-learn, etc … • Experience in using analytical concepts and statistical techniques: hypothesis development, designing tests/experiments, analyzing data, drawing conclusions, and developing actionable recommendations for business units. • Experience in developing production-grade ML systems including exploratory analysis, feature engineering, hyperparameter tuning, model training, model selection, creating data pipelines, etc. • Knowledge of AI tools and frameworks, including off-the-shelf GenAI solution builders (e.g., OpenAI GPT Builder, Microsoft Copilot Studio), GenAI developer tools (e.g., LangChain / LangGraph, LlamaIndex), off-the-shelf ML solution builders (SageMaker, Vertex AI, ML Studio), and ML developer tools (e.g., TensorFlow, PyTorch).
Benefits
• Startup environment that’s big on individual responsibility and leans on process and automation. • We’re big on culture. Work with stunning, supportive product, design and engineering teams on problems that matter. • You will be learning and growing all of the time. From business, product, design to engineering you will be learning from a world-class team that is caring, kind, and empathetic. • Mamo has the potential for a wide-reaching impact. Mamo is taking on the challenge of bringing about a new era of financial inclusion that begins close to home by providing access and experiences that make sense. That means you will never be bored.
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