Overview

Engineering Manager - Data Science

Responsibilities

• AI & ML Data Collection Leadership: Drive the execution of AI & ML initiatives related to data collection, ensuring that the team’s efforts are aligned with overall business goals and strategies.
• Technical Oversight: Provide hands-on technical leadership in the engineering of ML models and services, focusing on unstructured data, NLP, and classifiers.Oversee and contribute to the implementation of scalable solutions that meet high standards of reliability and efficiency.
• Team Leadership & Development: Lead, mentor, and develop a high-performing team of engineers and data scientists, fostering a culture of innovation and continuous improvement. Ensure effective communication and coordination within your team and across geographically dispersed teams.
• NLP Technologies: Contribute to the development and application of NLP techniques, including classifiers, transformers, LLMs, and other methodologies, to efficiently process and categorize unstructured data. Ensure these models are integrated seamlessly into the broader AI/ML infrastructure.
• Data Pipeline Engineering: Design, develop, and maintain advanced data collection pipelines, utilizing orchestration, messaging, database, and data platform technologies. Ensure pipelines are optimized for scalability, performance, and reliability.
• Cross-functional Collaboration: Work closely with other AI/ML teams, data collection engineering teams, product management, and others to ensure data collection efforts support broader AI/ML goals and product objectives.
• Innovation & Continuous Improvement: Continuously explore and implement new technologies and methodologies to enhance the efficiency and accuracy of data collection and processing systems. Stay at the forefront of advancements in NLP and data processing.
• System Integrity & Security: Ensure that all data collection systems meet the highest standards of integrity, security, and compliance. Implement best practices for data governance and model transparency.
• Talent Acquisition & Retention: Play an active role in recruiting, training, and retaining top engineering talent. Foster an environment where team members are encouraged to innovate, feel valued, and achieve their full potential.
• Process Improvement: Apply Agile, Lean, and Fast-Flow principles to improve team efficiency and the delivery of high-quality data collection solutions.
• Support Company Vision and Values: Model and promote behaviors that align with the company’s vision and values. Participate actively in company-wide initiatives and projects as required.

Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Data Science, or a related field.
• 6+ years of experience in software engineering, with a focus on AI & ML technologies, particularly in data collection and unstructured data processing.
• 3+ years of experience in a leadership role managing individual contributors.
• Strong expertise in NLP and machine learning, with hands-on experience in classifiers, large language models (LLMs), and other NLP techniques. Additionally experience in Gen AI, RAG, Agentic AI is essential
• Extensive experience with data pipeline and messaging technologies such as Apache Kafka, Airflow, and cloud data platforms (e.g., Snowflake).
• Expert-level proficiency in Java, Python, SQL, and other relevant programming languages and tools.
• Strong understanding of cloud-native technologies and containerization (e.g., Kubernetes, Docker) with experience in managing these systems globally.
• Demonstrated ability to solve complex technical challenges and deliver scalable solutions.
• Excellent communication skills with a collaborative approach to working with global teams and stakeholders.
• Experience working in fast-paced environments, particularly in industries that rely on data-intensive technologies (experience in fintech is highly desirable).