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Manager, Engineering, Data Science – Machine Learning
Morningstar. Execute AI and machine learning initiatives focused on data collection technologies .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI and machine learning, particularly in NLP, data collection, and unstructured data processing. Proven ability to lead multidisciplinary teams, manage cloud-native systems, and implement scalable data solutions while ensuring compliance and data governance.
Highest-signal resume keywords
AI & Machine Learning ExpertiseNLP & Classifiers ProficiencyCloud Data Platforms ExperienceLeadership & Team DevelopmentPython & SQL Programming
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI & Machine LearningNatural Language ProcessingData Collection TechnologiesUnstructured Data ProcessingData ArchitectureData PipelinesMLOpsCloud-Native TechnologiesApache KafkaAirflow
Soft Skills
Excellent CommunicationCollaborative ApproachMentoring & Leadership
Tools & Technologies
KubernetesDockerSnowflake
Industry Keywords
FintechAgileLeanFast-Flow PrinciplesData Governance
Tech Stack
Tools & technologiesAirflowApacheCloudDockerKafkaKubernetesPythonSnowflakeSQL
About the role
Key responsibilities & impact- Execute AI and machine learning initiatives focused on data collection technologies
- Lead the design, development, maintenance, deployment, and operationalization of AI/ML models, solutions, architecture, services, and data systems
- Provide hands-on technical direction for unstructured data processing, NLP, classifiers, GenAI, LLMs, MLOps, data architecture, data pipelines, and cloud-managed services
- Lead, mentor, and develop a multidisciplinary team of engineers and data scientists
- Build scalable and reliable systems and pipelines for processing and categorizing large volumes of unstructured data
- Design, develop, and maintain advanced data collection pipelines using orchestration, messaging, database, and data platform technologies
- Collaborate with AI/ML teams, data collection engineering, product management, and cross-functional stakeholders
- Explore and implement technologies and methodologies to improve data collection and processing efficiency and accuracy
- Ensure system integrity, security, compliance, data governance, and model transparency
- Recruit, train, and retain engineering talent
- Apply Agile, Lean, and Fast-Flow principles to improve team efficiency and delivery quality
- Promote company vision and values and participate in company-wide initiatives
Requirements
What you’ll need- Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Data Science, or a related field
- 8+ years of experience in software engineering, focused on AI & ML technologies, data collection, and unstructured data processing
- 3+ years of experience managing individual contributors in a leadership role
- Strong expertise in NLP and machine learning, including classifiers, LLMs, and other NLP techniques
- Extensive experience with Apache Kafka, Airflow, and cloud data platforms such as Snowflake
- Expert-level proficiency in Python, SQL, and other relevant programming languages and tools
- Strong understanding of cloud-native technologies and containerization, including Kubernetes and Docker
- Experience managing cloud-native systems globally
- Ability to solve complex technical challenges and deliver scalable solutions
- Excellent communication skills and a collaborative approach with global teams and stakeholders
- Experience working in fast-paced, data-intensive technology industries
- Fintech experience highly desirable
Benefits
Comp & perks- Hybrid work environment with in-person collaboration each week
- In most locations, four days in-office each week
- Tools and resources to engage meaningfully with global colleagues
- Limited corporate travel to remote offices or other business meetings and events
- Equal opportunity employer