Senior Data and Applied Scientist
The Senior Data and Applied Scientist at Pattern will design advanced ML models and simulations to optimize logistics/operations, collaborate cross-functionally, and drive data-driven business decisions with a focus on model performance and innovation.
Skills required
About this role
As a Senior Data and Applied Scientist, you will work with Pattern's Data Science team to curate
and analyze data, and apply machine learning models and statistical techniques to solve business
problems across the organization. Most of your work will revolve around logistics and operations.
What you’ll do:
● Design, build, and maintain predictive machine learning models and simulations to optimize
solutions across Pattern.
● Communicate potential model improvements, data needs, and opportunities across relevant
business units and developer teams.
● Continuously optimize the quality of our machine learning models.
● Conduct research to integrate new data sources, innovate in feature engineering, fine-tune
algorithms, and enhance data pipelines for robust model performance.
● Analyze large datasets to extract actionable insights that guide business decisions.
● Work closely with data science teams across different regions (US and India), ensuring seamless
collaboration and knowledge sharing.
● Follow code standards and best practices.
● Perform root cause analysis to determine accuracy defects and drive improvements.
What we’re looking for:
● Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Economics, or a
related field.
● 3-6 years of industry experience in building and deploying machine learning solutions and
simulations.
● 1-3 years of industry experience in logistics/operations preferred.
● Excellent communication skills (written and verbal) across departments with different levels of
machine learning knowledge.
● Strong data manipulation and programming skills in Python and SQL, and hands-on experience
with libraries such as Pandas, NumPy, Scikit-Learn, XGBoost.
● Strong problem-solving skills and an ability to analyze complex data.
● In-depth expertise in a range of machine learning and statistical techniques, such as linear and
tree-based models, along with understanding of model evaluation metrics.
● Understanding of linear programming, integer programming, and nonlinear programming.
● Experience with Git, AWS, and deep learning models is advantageous.
