Data Scientist Specialist - Customer Service

Textron•Wichita, KS

About The Position

Data Scientists drive strategic initiatives by performing advanced analytics, predictive modeling, and machine learning on complex internal and external datasets. They leverage advanced mathematics, statistics, and code-first tools to deliver high-impact, actionable insights. Unlike Data Analysts, Data Scientists focus on building and validating models, conducting experiments, and generating recommendations that directly inform business strategy and operational transformation. We are seeking an experienced Data Scientist to join our small, specialized, and highly motivated team that is implementing predictive maintenance and connected aircraft solutions for a large fleet of Textron Aviation aircraft. In this senior position, you will merge large scale time-series aircraft sensor data, parts, and maintenance records using a variety of machine learning, deep learning, AI, and visualization methods. You will keenly focus on maximizing the operational availability of customer jet, turboprop, and piston aircraft along with efficient aircraft maintenance practices. As a key part of your role, you will mentor and provide technical leadership to junior data scientists, foster creativity, and champion innovative, cutting-edge data science methodologies across the organization. At Textron Aviation, we are building a community of Data & Analytics professionals with an emphasis on collaboration and cross functional support. You will have the opportunity to work closely with your peers throughout the organization toward a vision of data driven strategy.

Requirements

Bachelor’s degree required in Applied Mathematics, Statistics, Computer Science, Data Science, or related technical field/coursework.
Minimum 7 years of relevant experience required in data science, machine learning, artificial intelligence, predictive modeling, or advanced analytics.
Excellent written and verbal communication skills with the ability to explain complex technical concepts and analytical findings to both technical and non-technical audiences.
Practical application experience with Python (and associated libraries: pandas, scikit-learn, etc.), SQL, and statistical analysis.
Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or similar).
Advanced proficiency with tree-based algorithms and dimensionality reduction.
Experience working with relational databases and developing complex data sets.
Experience with generative AI concepts, including large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and comfortable with AI coding assistants.
Experience building, deploying, and managing machine learning solutions using cloud-native platforms (Azure ML, MLflow, AWS SageMaker, etc.), including MLOps practices such as version control, CI/CD, experiment tracking, model deployment, monitoring, and lifecycle management.
Commitment to continuous learning, adoption of emerging data science methodologies, and ethical, responsible AI practices.
Experience optimizing compute performance on large datasets using parallel processing and multi-core utilization techniques and packages (e.g. Polars, PySpark, Dask, etc.)
Experience with deep learning libraries (e.g. PyTorch, TensorFlow), transformer-based architectures, and advanced modeling techniques.
Strong linear algebra skills.
Proven experience leading data science projects and teams, mentoring junior data scientists, and driving strategic initiatives.
Strong analytical skills, research capabilities, and careful attention to detail.

Nice To Haves

Master's or PhD degree in Applied Mathematics, Statistics, Computer Science, Data Science, or related field preferred.
Aviation experience preferred.
Preferred experience in ML model training on multiple GPUs/CUDA.

Responsibilities

Support data exploration, hypothesis testing, and foundational predictive analytics.
Independently develop machine learning models for classification, regression, clustering, and time-series analysis using Python and relevant libraries.
Collaborate with analysts and engineers to prepare and clean data for modeling.
Document methodologies and contribute to reproducible research practices.
Develop foundational understanding of the business domain, including aircraft OEM operations.
Apply advanced statistical techniques to evaluate model performance and business impact.
Work with large datasets using Python, SQL, and cloud-native platforms.
Communicate findings through technical documentation and stakeholder presentations.
Contribute to the development of analytics best practices and model governance.
Lead development of predictive models and advanced analytics workflows.
Mentor junior data scientists and contribute to team knowledge sharing.
Design experiments and causal inference studies to support strategic initiatives.
Collaborate with cross-functional teams to integrate models into business processes.
Champion adherence to governance frameworks and global data science standards.
Drive innovation in data science methodologies and tools.
Influence enterprise-wide strategy through advanced modeling and simulation.
Lead cross-domain projects involving deep learning, NLP, or advanced time-series forecasting.
Advocate for ethical AI practices and model interpretability.
Serve as a thought leader in data science governance, ensuring consistency and quality across teams.