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.