Data Lead

2 months ago

Kitchener, Canada FluidAI Medical Full time

**Job Title**

Data Lead

**Summary**

This role centers around leading the Data Team at FluidAI to extract maximum value from our proprietary sensors and system's data, ultimately aiming to enhance patient outcomes. As we roll-out our smart monitoring tool to customers, we are looking for a Data Lead who can take ownership of the scaling of our data architectures and guide our high-performing Data Team towards continued success.

It's not everyday that you get a chance to make a direct impact on people's health. If you're looking to join a strong team for a meaningful product, this is for you

**Who We Are**

FluidAI aims to create a paradigm shift in post-operative care, utilizing the plethora of data within the body, to help in the detection of post-operative complications at their onset. FluidAI’s platform sensing technology augments existing wound drains and catheters, providing healthcare providers with a smart monitoring tool that can enhance the delivery of care. FluidAI’s technology is being built with the goal of decreasing surgeons’ response time to potential complications, making patient recovery easier, and hopefully saving lives.

**Why join Fluid AI?**
- Mission to save lives and improve patient care with technology that is already deployed on patients in real-world settings.
- Work with a high-performing and cross-functional team that is empowered to grow, learn, and contribute across all levels of the organization.
- Personal career-focused development and training with access to workshops, coursework, and experienced mentors/consultants.
- Health and wellbeing benefits, including vision, drugs, dental, and life insurance.
- Regular offsite activities - past events include rafting, axe-throwing, sports leagues, and the usual BBQ.
- Unlimited vacation and paid parental leave.
- Flexible on-site/WFH hybrid setup.

**Duties and Responsibilities**
- Drive FluidAI’s data strategy in alignment with our business objectives and in collaboration with our teams across research, product development, clinical studies, and sales and operations.
- Champion, design, and build scalable and robust data architectures with traceable “single source-of-truth” datasets.
- Evaluate and recommend the best tools and technologies to effectively manage FluidAI’s data stack.
- Train and mentor teams from diverse backgrounds on data engineering/machine learning best practices, tools, and technologies.
- Take initiative and partner with cross-functional teams to find additional opportunities where we can consume and leverage data to improve our overall business KPIs.
- Prepare technical data reports and summaries for regulatory submissions or internal/external presentations on an as-needed basis.

**Skills/Qualifications**
- 5+ years of data engineering experience designing scalable and robust data architectures.
- 1+ years of leadership experience on a data team, showcasing the ability to foster collaboration and drive data-driven initiatives.
- Experience working closely with ML teams for model training and model deployment at a production scale.
- Experience designing batch processing pipelines with tools such as Spark and experience with workflow management tools like Airflow or Beam.
- Demonstrated proficiency with at least one of the big 3 (Azure/AWS/GCP) cloud providers.
- Proficient in utilizing project management tools such as Jira or Notion to effectively plan, track, and manage data-related projects.
- Proven excellence in technical and non-technical oral and written communication.
- Ability to work in a high-pressure environment and constantly changing conditions.

**Bonus Qualifications**:

- Familiarity with DevOps and DataOps practices.
- Experience with containerization technologies such as Docker and Kubernetes.
- Familiarity with data privacy and security regulations such as HIPAA and GDPR.
- Experience using data build tool (dbt) to transform data in a data warehouse.
- Experience building analytical tools, using tools such as Power BI or Tableau, to draw metrics and provide actionable insights from data pipelines.
- Solid understanding of machine learning techniques, particularly for prediction and classification.
- Strong background in statistics, including expertise in statistical inference, experimental design, hypothesis testing, regression analysis, and time series analysis.

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