Senior Data Scientist (Architect)

Ektello; Austin (TX); On-site; USD 110,000 - 124,000; Part time

Job summary

American Automotive Company seeks a Senior Data Scientist (Architect) for a 4-month contract in Austin, onsite. The role involves applying statistics, ML, and optimization to manufacturing data to improve safety, quality, throughput, cost, and reliability.

Responsibilities include creating analytical datasets, dashboards, and productionizing models with a focus on measurable business impact across plants, with close collaboration to IT and operations teams.

Qualifications

• Education - Bachelor's degree in a technical field required.
• 8–10 years of experience in data modeling, pipelines, and analytics.
• Experience with SQL, Python, Spark, and Databricks is expected.

Responsibilities

• Translate plant/business problems into measurable analytical questions.
• Build reliable datasets and pipelines using SQL, Python, Spark, and Databricks.
• Develop and deploy predictive/prescriptive models for downtime, defects, and maintenance.

Skills

Data modeling

Data pipelines

Data analytics

Education

Bachelor's degree in a technical field

Tools

SQL

Python

Spark

Databricks

Job description

Job Title: Senior Data Scientist (Architect)

Client: American Automotive Company

Duration: 4 months contract with HIGH chance for extension or become permanent

Location: Austin, TX (Onsite)

Employment type: W2 only (Not open to C2C or 1099)

Pay: $80-90/hr w2 + Benefits/PTO

Top Required Skills

• Data modeling at least 8-10 years of experience
• Data pipeline at least 8-10 years of experience
• Data analytics – able to build something out of messy data at least 8-10 years of experience

Position Overview

The Data Scientist applies statistics, machine learning, optimization, and programming to manufacturing data to improve safety, quality, throughput, cost, equipment reliability, and decision‑making across plants. The role partners closely with Manufacturing IT, plant operations, engineering, quality, maintenance, and data engineering teams.

Key Responsibilities

• Translate plant and business problems into measurable analytical questions and use cases.
• Identify, access, and assess data from MES, quality systems, equipment historians, maintenance systems, production systems, and other manufacturing sources.
• Build reliable analytical datasets and pipelines using SQL, Python, Spark, and Databricks.
• Perform exploratory analysis, statistical studies, root‑cause analysis, forecasting, optimization, and experimentation.
• Develop, validate, document, and monitor predictive or prescriptive models for use cases such as downtime, scrap, defects, bottlenecks, anomaly detection, yield, and preventive maintenance.
• Evaluate data quality, lineage, coverage, missingness, bias, and operational readiness before modeling.
• Convert findings into practical recommendations that plant personnel and leaders can use in daily decisions.
• Create dashboards, visualizations, reports, and user interfaces that clearly communicate trends, risks, and opportunities.
• Productionize analytics and models in partnership with data engineering, application, and Manufacturing IT teams.
• Monitor model performance, data drift, pipeline health, and business impact after deployment.
• Support manufacturing modernization initiatives, including cloud migration, data‑product development, automation, and legacy‑system retirement.
• Follow data, cybersecurity, AI governance, safety, privacy, and change‑management requirements.
• Present technical results to both technical and nontechnical audiences and maintain clear documentation.
• Promote reusable analytical methods, standards, and best practices across plants and manufacturing domains.

Qualifications

• Education - Bachelor's degree in a technical field such as computer science, computer engineering or related field required.
• Years of experience – at least 8-10 years of experience Experience technologies including SQL, Python, Spark, and Databricks Data modeling at least 8-10 years of experience Data pipeline at least 8-10 years of experience Data analytics – able to build something out of messy data at least 8-10 years of experience
• Data modeling at least 8-10 years of experience
• Data pipeline at least 8-10 years of experience
• Data analytics – able to build something out of messy data at least 8-10 years of experience