Senior Data Scientist

KYYBA Inc; Austin (TX); On-site; USD 95,000 - 120,000; Full time

Benefits offered by this job

401k

Life insurance

Opportunity for advancement

Job summary

Kyyba Inc is seeking a Senior Data Scientist specializing in Manufacturing Operations to analyze factory data, build models for predicting issues, and collaborate with various teams. The role involves employing statistical methods and machine learning to interpret complex datasets and improve process efficiencies.

The ideal candidate has over 6 years of relevant experience, proficient in Python and SQL, and is capable of translating findings into actionable insights.

Qualifications

• 6+ years of applied data science experience on real operational datasets.
• Strong skills in Python and SQL for handling large datasets.
• Ability to build and validate statistical and ML models.

Responsibilities

• Frame manufacturing problems with engineering partners.
• Explore and clean fragmented operational data.
• Build and validate statistical and machine-learning models.
• Provide usable outputs to engineers and operators.

Skills

Data modeling

Data pipeline

Data analytics

Python

SQL

Statistical modeling

Machine learning

Feature engineering

Education

Bachelor’s or master’s in a quantitative or engineering field

Tools

Databricks

Spark/PySpark

Job description

Founded in 1998 and headquartered in Farmington Hills, MI, Kyyba has a global presence delivering high-quality resources and top-notch recruiting services, enabling businesses to effectively respond to organizational changes and technological advances.

At Kyyba, the overall well-being of our employees and their families is important to us. We are proud of our work culture which embodies our core values; incorporating value, passion, excellence, empowerment, and happiness, creates a vibrant and productive atmosphere. We empower our employees with the resources, incentives, and flexibility that they need to support a healthy, balanced, and fulfilling career by providing many valuable benefits and a balanced compensation structure combined with career development.

Job Description:

Position Description:

Senior Data Scientist — Manufacturing Operations

We are hiring a Senior Data Scientist to work with factory and industrial data: find what is going wrong (or about to), build models that hold up against plant reality, and help the team act on them.

Most of the job is in the data — understanding how a process behaves, cleaning noisy and incomplete signals, defining “normal” vs “abnormal” with people who run the line, creating features, validating against real outcomes, and explaining limits when the data cannot support a model. You will partner with manufacturing, quality, maintenance, data engineering, and software. You will not own the data platform. This is not a research lab role and not a platform-engineering role.

What we are hiring for:

Someone who can walk a real example: this was the grain of the data, this is what I found, this is the model, this is how I knew it was wrong or right, this is what operations did with it.

Typical problems: process drift, abnormal machine behavior, quality prediction, equipment health, bottlenecks, downtime, scrap/rework, root-cause support. Methods follow the problem (statistical limits, clustering, isolation forest, time series, autoencoders, supervised models when labels exist) — we do not hire to a method list.

Manufacturing experience is a plus. We will also consider people from industrial IoT, equipment, quality, automotive, semiconductor, energy, telecom/ops, or similar operational environments who have done this loop on messy sensor or process data.

Responsibilities:

• Frame manufacturing problems with plant and engineering partners; push back when labels, ground truth, or “accuracy” expectations are not real.
• Explore, clean, and join fragmented operational data (machines, sensors, quality, maintenance, production, MES/historian extracts — you do not need to have used every acronym).
• Build and validate statistical and machine-learning models for anomaly, quality, health, and process monitoring; report false positives/negatives and business cost, not only a leaderboard metric.
• Hand usable outputs to engineers and operators (thresholds, explanations, “what to do when this fires”), and support models after they are in use.
• Work with data engineering and software on pipelines, Databricks, and production — you are the customer of the platform, not the person hired to build it.

Required qualifications

• Bachelor’s or master’s in a quantitative or engineering field (data science, CS, statistics, industrial/mechanical/manufacturing engineering,OR, applied math, or related).
• 6+ years of applied data science (analysis, feature work, statistical or ML modeling on real operational or business datasets). Count data-science years, not total years in IT, DBA, or software engineering.
• Strong Python and SQL; evidence of working large, messy tables — not only notebooks on clean extracts.
• Production of models you can defend: classification, regression, clustering, anomaly detection, or time series, with a clear target and validation approach.
• Experience creating features from machine, sensor, process, quality, maintenance, or other operational data (industrial preferred; high-volume ops data from adjacent domains is acceptable).
• Comfort telling stakeholders when a model should not ship.
• Ability to learn an unfamiliar plant process quickly.

Preferred qualifications

• Time in manufacturing, industrial IoT, semiconductor, automotive, aerospace, energy, or equipment-heavy operations.
• Databricks, Spark/PySpark, or similar cloud analytics (we use Databricks; we do not require you to have been the lakehouse owner).
• Familiarity with MLOps (tracking, monitoring, drift) as a partner to platform teams.
• SPC, explainability, or prior work with historians/MES data.

Candidate Requirements

• Education - Bachelors 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
• Application AI platform skill set is a nice to have, not required

Top 3 must-have hard 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

Location: (Onsite Position and Austin TX)

Disclaimer:

Kyyba is an Equal Opportunity Employer.

Kyyba does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non‑disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. Minorities / Females / Protected Veterans / Individuals with Disabilities are encouraged to apply. All employment is decided on the basis of qualifications, merit, and business need.”

It is the policy of Kyyba to provide reasonable accommodation when requested by a qualified applicant or employee with a disability, unless such accommodation would cause an undue hardship. The policy regarding requests for reasonable accommodation applies to all aspects of employment, including the application process. If reasonable accommodation is needed, please contact Kyyba at 248‑813‑9665

Rewards:

• 401k
• Term life
• Voluntary life and disability insurance
• Optional Pre‑paid legal plan
• Optional Identity theft planOptional Medical and dependent FSA
• Opportunity for advancement
• Long‑term assignment with opportunity for hire by client

SELECT AWARDS

• An INC 5000 company for 10 years
• Corp! Michigan Economic Bright Spots
• Crain’s Detroit Business Top Staffing Service Companies in Detroit
• TechServe Alliance Excellence Award- IT and Engineering Staffing & Solutions
• Best of MichBusiness winner in HR Wizards & Partnerships
• Metro Detroit Elite Category: Recruitment, Selection & Orientation for 101 Best & Brightest
• 101 Best & Brightest Companies to Work for in Michigan