Senior AI Engineer - Life Sciences

Create revenue growth plan for Network MDM

Advise product team on strategy and priorities

Apply ML/AI techniques in healthcare SaaS.

Collaborate with cross-functional teams.

Apply ML/AI in healthcare systems

Collaborate with a diverse team

Lead project initiatives effectively

Drive execution and maintain project timelines

Own initiatives and projects from start to finish

Create project plans and drive execution

Lead project initiatives from start to finish

Create detailed project plans and drive execution

Investigate child safety violations

Develop detection signals using SQL, Databricks, Python

Standardize data infrastructure

Develop scalable data pipelines

Be the single source of truth on business health, KPIs, and performance trends

Blend technical analysis with storytelling to guide data-driven decisions

Support FP&A with models and automation

Collaborate with departments to monitor performance

Developing data pipelines

Collaborating with cross-functional teams

Own the technical side of the sales cycle

Act as a trusted advisor to customer stakeholders

Ownership of data products for decision-making insights

Collaboration with data scientists, engineers, and stakeholders

Own data products and deliver insights to drive business decisions

Lead requirements elicitation and collaborate with data teams

Design, build, and own Hazel’s GTM stack

Build automated workflows for lead scoring, lifecycle marketing, and outbound prospecting

Scalable liquidity planning

Hands-on monitoring of liquidity and buffers

Understand customer data needs and goals

Build data pipelines and workflows

Model and document new datasets

Automate business metrics

Work with clients on big data challenges

Provide data engineering, data science, and cloud projects

Model and document new datasets

Automate business metrics

Design, build, deploy, and monitor production-ready ML services.

Collaborate with AI scientists to package and deploy ML models.

Lead design and sales of AI solutions on AWS

Engage C-level executives with AI demos

Define and measure performance metrics

Develop analytics strategies

Design and build GRC automation processes

Integrate multiple technologies/APIs for control checks

Build and scale ETL/ELT pipelines and compute infra across clouds.

Ingest and integrate new data sources for analytics and ML.

Identify and acquire new business across the 4th Estate

Secure new logos and drive revenue growth

Model and document new datasets

Automate business metrics

Provide technical leadership and value to sales

Drive technical discovery and solution design for strategic accounts

Lead architecture and build high-scale data infrastructure and pipelines

Mentor engineers and promote data-engineering best practices

Lead the architecture, delivery, and adoption of a knowledge platform

Design and scale semantic and operational layers on a Databricks lakehouse

Define and drive long-term data architecture, modeling, and transformation

Establish reliability, observability, and quality standards across pipelines

Model and document new datasets to drive business value

Automate business metrics for success alignment

Drive data velocity and production efficiency

Ensure end-to-end ownership of data lifecycle

Secure new Enterprise accounts

Develop focused strategy for Navy accounts

Lead design, implementation, and maintenance of scalable ML infrastructure

Manage ML Model Deployment & Platform Management on Databricks

Model and document new datasets

Automate business metrics alignment

Deliver technical and business value to clients

Engage clients through demos and integrations

Build and support high-performance data solutions

Tackle complex technical challenges in data engineering

Design and run growth experiments

Collaborate with cross-functional teams

Design and implement robust data models for analytics

Collaborate with stakeholders to deliver data products

Define long-term architecture for ML platform

Establish standards for code quality and testing

Build and maintain data platform capabilities

Contribute to data lake modernization

Drive revenue growth through pricing and experiments.

Analyze device data to shape strategy and product impact.

Design and operate ML infrastructure

Productionize ML workloads

Design and develop data pipelines using Python, Spark, Databricks, SQL, and SSIS.

Implement ETL/ELT workflows with Apache Airflow and SSIS.

Analyze customer datasets for insights

Explain model performance and risk policies

Build and manage AI tooling stack and system infra for Field Engineering

Set standards for AI-driven development and scale capabilities

Architect data governance frameworks for compliance data risk

Enforce data contracts between Producers and Compliance/CX/IOS

Design and develop Power BI reports and dashboards aligned to business goals

Build and maintain reusable Power BI assets

Mature the Analytics function at Jellyfish

Drive data-informed decision-making across various teams