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Data Scientist

Job in Grand Prairie, Dallas County, Texas, 75051, USA
Listing for: Vastika Inc
Full Time position
Listed on 2026-08-28
Job specializations:
  • IT/Tech
    Data Scientist, Data Engineering, Data Analyst, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 120000 - 180000 USD Yearly USD 120000.00 180000.00 YEAR
Job Description & How to Apply Below

Role: Data Scientist

Location: Grand-Prairie, Texas 75050

Work Model:
Onsite (4 days a week work from office Mon - Thu)

Duration: Fulltime Employee

Numbers of Interview
: 3 to 4

Mode of Interviews – Virtual

Tentative start date – ASAP

Note
:
Relocation assistance will be provided

Must Have
  • 5+ years of experience required as Data Scientist (No limit for a right candidate)
  • Strong SQL and Python proficiency with hands-on experience in medallion/Lakehouse architectures on Databricks, Snowflake, AWS, or Azure.
  • Data Science Proven track record building and deploying ML models for predictive maintenance, anomaly detection, demand forecasting, and root cause analysis.
Job Overview
  • Data Engineering Skilled in building scalable cloud data pipelines for high-volume manufacturing and IoT data using Spark, Kafka, Airflow, and Delta Lake.
  • Strong communicator — able to translate complex model outputs into clear, actionable recommendations for operations and executive stakeholders.
  • Experience designing A/B experiments and simulations to validate process changes and quantify business impact before full deployment.
Good To Have Skills
  • 2-4 years working in manufacturing domain.
  • Experience in shop floor operations, production planning, and systems including MES, SCADA, and ERP. Proficient in industrial protocols (OPC-UA, MQTT, Modbus) with ability to bridge OT/IT systems for real-time data extraction.
  • Applied experience with OEE, Six Sigma, SPC, and lean methodologies to drive measurable gains in yield, uptime, and efficiency.
  • Proficient in scikit-learn, Tensor Flow, or PyTorch with experience moving models from prototype to production in industrial environments.
  • Solid grounding in statistical methods — time series, regression, clustering, and hypothesis testing applied to manufacturing quality problems.
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