Data Scientist
First seen by Alion on Oct 23, 2026.
We’re looking for a Senior Data Scientist who can take a data problem from an initial business question to a solution that works in production.
This role is not only about building models. We’re looking for someone who is comfortable exploring imperfect data, challenging assumptions, choosing the right approach for the problem, and working closely with engineers and product teams to turn experiments into reliable solutions.
What you’ll work on:
Explore and analyze complex datasets to understand patterns, dependencies, and opportunities.
Turn business and product questions into well-defined data science problems.
Design, train, evaluate, and improve machine learning and statistical models.
Work on the full ML lifecycle: data exploration, feature engineering, experimentation, validation, deployment, and monitoring.
Decide when an ML model is actually needed — and when a simpler statistical or analytical approach will do the job better.
Define meaningful metrics and design experiments to measure the impact of proposed solutions.
Work with Data and ML Engineers to bring models into production and keep them reliable over time.
Investigate model performance, data quality issues, drift, and unexpected production behavior.
Present findings and recommendations in a way that is useful to both technical teams and business stakeholders.
Help shape our data science practices through code reviews, technical discussions, and mentoring.
What we’re looking for:
5+ years of hands-on experience in Data Science or Machine Learning.
Strong Python skills and practical experience with Pandas, NumPy, scikit-learn, or similar tools.
Strong foundation in statistics, probability, and machine learning.
Experience working with real-world datasets where the data is not always clean, complete, or perfectly structured.
Confident SQL skills.
Experience building ML solutions beyond notebooks and prototypes.
Good understanding of model validation, experimentation, feature engineering, and performance evaluation.
Experience with production ML workflows and familiarity with MLOps practices.
Experience with AWS, GCP, or Azure.
Ability to explain technical decisions, trade-offs, and results clearly.
English — Upper-Intermediate or higher.
It would be a plus if you have:
Hands-on experience with LLMs / Generative AI, NLP, recommendation systems, forecasting, or computer vision.
Experience with PyTorch or TensorFlow.
Experience with tools such as Spark, Databricks, Snowflake, MLflow, or Airflow.
Experience designing A/B tests and working with product experimentation.
Experience mentoring other Data Scientists or taking ownership of technical decisions.