Data Scientist

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 by Client.

As part of our commitment to our employees, Nagarro provides a robust benefits package for full-time employees, which includes:

• Medical Coverage, Dental and Vision (100% Nagarro contribution for the employee, 80% contribution for immediate dependents).
• 15 days of Paid Time Off (PTO).
• 10 paid holidays (including 8 fixed holidays and 2 floating holidays).
• 401K enrollment with a 100% employee contribution (please note that we do not provide a matching contribution).

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, TensorFlow, 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.