Company
Technology
DataScience&EngineeringLead
Neural analysis suggests this role is
optimal for Lead candidates.
“Data Science & Engineering Lead. Skills: Machine Learning, Data Engineering, MLOps, Cloud Architecture. Lead design of ML models. Develop ML models”
What You'll Achieve.
Deliver actionable business insights
Industry & Context.
What They're Looking For.
Must Have
7+ years of experience, Bachelor’s degree, SQL proficiency, Data modeling for OLTP and OLAP systems
Nice to Have
Master’s or PhD preferred, AWS or Azure certifications
What You'll Do.
Lead design of ML models
Architect data pipelines
Optimize data pipelines
Build lakehouse architectures
Maintain lakehouse architectures
Implement data modeling standards
Lead MLOps initiatives
Manage ML model lifecycle
Develop distributed workflows
Optimize distributed workflows
Oversee database design
Optimize database design
Drive cloud architecture decisions
Implement cloud architecture
Build analytics solutions
Deliver business insights
Mentor junior engineers
Guide junior engineers
Promote AI/ML engineering best practices
Promote data architecture best practices
Promote software development best practices
How You'll Work.
Team & Collaboration
Cross-functional technical teams
Communication Scope
Collaborate with stakeholders
Full Job Description
## Accountabilities Lead the design, development, and deployment of advanced machine learning models across supervised, unsupervised, deep learning, NLP, and computer vision use cases. Architect and optimize scalable data pipelines for batch and streaming data processing using modern frameworks and tools. Build and maintain lakehouse architectures (Delta/Iceberg) and implement bronze-silver-gold data modeling standards. Design and manage ETL/ELT workflows using tools such as Airflow, DBT, and Airbyte to ensure reliable data transformation and movement. Lead MLOps initiatives, including model deployment, monitoring, and lifecycle management using platforms like Databricks, SageMaker, or Azure ML. Develop and optimize distributed data processing and training workflows using Spark, EMR, Glue, and similar big data technologies. Oversee database design and optimization across OLTP and OLAP systems, including relational and NoSQL databases. Drive cloud architecture decisions and implementations on AWS or Azure, including infrastructure components and data services. Build BI and analytics solutions using tools like Power BI, Tableau, or QuickSight to deliver actionable business insights. Mentor and guide junior engineers, promoting best practices in AI/ML engineering, data architecture, and software development. Requirements: 7+ years of experience in data science, machine learning, or data engineering roles with proven leadership responsibilities. Strong expertise in supervised and unsupervised learning, deep learning, and neural network architectures (CNNs, RNNs, GANs, Transformers). Hands-on experience with ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. Proven experience building and deploying production-grade ML systems using MLOps platforms (Databricks, SageMaker, Azure ML). Strong knowledge of big data processing frameworks such as Apache Spark, EMR, and AWS Glue. Expertise in designing ETL pipelines and data workflows using
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