Data Engineer / Data Scientist

Saileor
Greater Hyderabad Area

About the job

Saileor is hiring a Data Engineer / Data Scientist on behalf of our client, an industrial AI company based in Germany.

This role is based in our Hyderabad office. Germany is 3.5 to 4.5 hours behind India, so your working day overlaps well with the client’s team.

You will turn raw sensor and camera data into decisions the client’s platform can act on.

Experience & Qualifications

  • Strong Python skills, end to end
  • Hands-on experience with pandas and numpy for signal and time-series work
  • Exposure to SciPy or scikit-learn
  • Sound ML fundamentals for classification and anomaly detection on noisy, real-world dataGood to Have
  • Computer vision experience, traditional or VLM-based
  • Signal processing, such as filtering and feature extraction
  • Experience with industrial, IoT or sensor dataStrong generalists with time-series or applied ML backgrounds are welcome, if they are keen to learn the domain.

Key Responsibilities

  • Clean and model high-frequency vibration and machine telemetry stored in TimescaleDB, at millions of rows a day
  • Build and refine cycle and pattern detection pipelines using motif detection, sequence similarity and clustering
  • Turn this work into features for predictive maintenance and OEE analytics
  • Use local vision-language models to classify frames from fixed industrial cameras, such as detecting if a product is present on a production line
  • Work close to raw sensor streams and real production-floor limits, not pre-cleaned datasets
  • Work closely with the client’s team in GermanyPersonal Attributes
  • Curious and comfortable with messy, real-world data
  • Reliable, accountable, and able to work well across time zones and teamsWhat’s on Offer
  • Work on real industrial AI problems with live production data
  • Hands-on exposure to both time-series ML and computer vision
  • Experience working with a European technology company
  • Opportunity to grow into more senior responsibilities over time based on performance

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