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Role Overview

This role is primarily focused on data science and machine learning, with a strong emphasis on developing and optimizing algorithms, predictive modeling, and anomaly detection. At the same time, data engineering plays a crucial role in ensuring our models are built on a strong and scalable infrastructure.

Your first task? Taking the lead in developing and enhancing Wint's machine learning algorithm, ensuring its accuracy, efficiency, and scalability. Following that, you will take ownership of structuring the dataā€”cleaning, organizing, and building DAGs and data pipelines to establish a robust foundation for our AI systems.

Key Responsibilities

• Algorithm Development & ML: Implement, optimize, and support a new machine learning algorithm in production.
• Automation & Anomaly Detection: Develop real-time data pipelines, anomaly detection, and predictive modeling solutions.
• Data Structuring & Cleansing: Work with raw data, clean and transform it into a usable format for models and business insights.
• Data Engineering & Pipelines: Own and improve data pipelines, ensuring efficient and scalable data flows (Airflow, ETL).
• Cross-Team Collaboration: Work closely with engineering, product, and business teams to ensure alignment on data-driven initiatives.
• End-to-End Ownership: Drive projects independently, ensuring accountability from data processing to model deployment.

Required Skills & Qualifications

āœ… 5+ years of experience in data science, ML, or data engineering.
āœ… Strong proficiency in Python & SQL (Must-Have).
āœ… Experience with ML frameworks & signal processing.
āœ… Hands-on experience with cloud platforms (AWS preferred).
āœ… Knowledge of Airflow (or similar workflow orchestration tools).
āœ… Strong problem-solving skills, ability to work independently, and take full responsibility for tasks.

āœ…Data Engineering & ETL experience (building scalable data workflows).
āœ…Experience with predictive modeling, anomaly detection, or productionizing ML models.

Work Environment & Culture

šŸ»ć Hybrid work model ā€“ flexibility to work remotely while engaging with the team on-site when needed.
šŸ¤ Highly collaborative and fast-paced environment.
šŸŽÆ Strong emphasis on ownership, autonomy, and impact-driven work.