The Role
Build and maintain data enrichment pipelines, curate and label datasets, resolve data quality issues, prototype APIs/tools, collaborate with ML/product teams, and document reproducible workflows for large, messy industrial data.
Summary Generated by Built In
Why join us
Data Science at TRACTIAN
What you'll do
Responsibilities
Requirements
Technical Skills
Nice to Have
TRACTIAN is transforming the industrial world by empowering frontline maintenance workers to achieve more. We’ve fused cutting-edge hardware with innovative software into one powerful platform, disrupting legacy systems and delivering smarter, faster solutions for our clients.
The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data. Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies. The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.
We’re hiring Data Foundry Engineers to join Tractian’s Machine Learning Engineering team.
This role focuses on building and improving datasets used across the company. The team works across data engineering, back-end, front-end, product, labeling, and AI engineering to make information more structured, reliable, and useful for internal systems, clients, and AI applications.
Tractian processes more than 20 million industrial data samples per day. We are looking for someone who is comfortable working close to the data: understanding source quality, identifying inconsistencies, improving processing logic, and helping turn fragmented information into cohesive datasets.
- Improve and maintain data enrichment pipelines built in Python
- Work on data curation tasks such as labeling, deduplication, normalization, and inconsistency resolution
- Investigate data quality issues by querying and combining data from multiple databases
- Prototype tools, APIs, and workflows to support data operations
- Use and create AI-based tools responsibly to support data tasks, including reviewing automated outputs when needed
- Collaborate with product, data, and machine learning teams on data quality and usability
- Document workflows and methodology to keep processes reproducible and auditable
- 3+ years of experience in data engineering, software engineering, machine learning engineering, or similar roles
- Strong Python skills for data processing and automation
- Solid experience with Pandas and tabular data manipulation
- Experience with data curation, enrichment, labeling, or quality control workflows
- Experience working with large or messy datasets
- Ability to evaluate whether data outputs are correct, consistent, and useful
- Comfortable working across new data domains and switching context between different datasets and workflows
- Organized and methodical approach to technical work
- Experience with APIs and service integration
- Familiarity with FastAPI or similar frameworks for internal APIs
- Familiarity with SQL and/or NoSQL databases
- Familiarity with workflow orchestration tools such as Airflow or Temporal
- Familiarity with AI-assisted workflows, including using LLMs or agents for data tasks and reviewing automated outputs
- Experience with spreadsheets and tabular analysis
- Experience with web scraping or extracting data from semi-structured sources
- Experience with Streamlit or similar tools for internal prototypes
- Experience with Selenium, Playwright, or similar browser automation tools
- Familiarity with industrial, maintenance, or operational data
- Familiarity with gRPC is a plus
- Experience with annotation or labeling workflows
- Background in scientific or highly reproducible technical work
Skills Required
- 3+ years of experience in data engineering, software engineering, machine learning engineering, or similar roles
- Strong Python skills for data processing and automation
- Solid experience with Pandas and tabular data manipulation
- Experience with data curation, enrichment, labeling, or quality control workflows
- Experience working with large or messy datasets
- Ability to evaluate whether data outputs are correct, consistent, and useful
- Comfortable working across new data domains and switching context between different datasets and workflows
- Organized and methodical approach to technical work
- Experience with APIs and service integration
- Familiarity with FastAPI or similar frameworks for internal APIs
- Familiarity with SQL and/or NoSQL databases
- Familiarity with workflow orchestration tools such as Airflow or Temporal
- Familiarity with AI-assisted workflows, including using LLMs or agents for data tasks and reviewing automated outputs
- Experience with spreadsheets and tabular analysis
- Experience with web scraping or extracting data from semi-structured sources
- Experience with Streamlit or similar tools for internal prototypes
- Experience with Selenium, Playwright, or similar browser automation tools
- Familiarity with industrial, maintenance, or operational data
- Familiarity with gRPC
- Experience with annotation or labeling workflows
- Background in scientific or highly reproducible technical work
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The Company
What We Do
Tractian is a machine-intelligence company delivering integrated hardware, cloud software and AI to prevent machine failures and boost industrial uptime. Their offering combines vibration and condition sensors, TracOS maintenance-management software, and AI-driven analytics to enable predictive maintenance, energy optimization and operational visibility for factories and asset-heavy operations globally.
