Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
Role Overview
We are looking for an experienced Software Engineer with strong skills in Python, algorithm development, scientific computing, and data-driven solution development.
The ideal candidate will contribute to the design, implementation, validation, and productization of algorithms that transform complex diagnostics and operational data into actionable insights. The role will work closely with senior engineers, domain experts, data engineers and platform teams to develop solutions for data analysis, signal and image processing, predictive analytics, and machine learning.
As our data platform evolves from the current AWS-based environment toward Databricks, the candidate will also contribute to migrating and modernizing algorithmic and analytical workloads. This includes adapting Python-based computational workflows for distributed processing, developing reusable features for analytics and machine learning, and applying modern ML development practices on Databricks.
This role does not own core data ingestion or ETL pipelines. It focuses on the analytical and algorithmic layer that consumes curated data provided by the data exploration/engineering platform.
Key Responsibilities
Develop and maintain algorithms for processing, analyzing, and modeling machine diagnostics and operational data.
Build Python-based computational workflows using appropriate scientific computing and data analysis.
Analyze complex datasets to identify patterns, derive useful features, and support predictive or diagnostic insights.
Develop and improve algorithms in areas such as signal processing, image analysis, anomaly detection, predictive analytics, and other data-driven applications.
Perform algorithm testing, validation, benchmarking, and performance evaluation against defined criteria.
Collaborate with domain experts and data scientists to translate technical and scientific problems into practical analytical solutions.
Work with data engineers to understand data structures, data quality, and curated datasets required by algorithms.
Develop reusable, modular, testable, and maintainable Python components.
Improve the performance and scalability of analytical workloads under the guidance of senior engineers and platform specialists.
Contribute to the migration of existing algorithmic and analytical workloads from the current AWS-based data platform to Databricks.
Adapt existing Python and analytical workloads to work with Databricks, Spark/PySpark, Delta-based datasets, and distributed compute where appropriate.
Develop and maintain reusable feature-generation logic for model development and analytical applications.
Apply structured ML lifecycle practices such as experiment tracking, reproducibility, model evaluation, versioning, and deployment.
Support the productionization of algorithms and machine learning models through repeatable and automated workflows.
Participate in code reviews, technical design discussions, estimation, testing, and implementation planning.
Follow software engineering practices including source control, automated testing, CI/CD, documentation, and observability.
Continuously build knowledge of Databricks, distributed processing, and modern ML engineering practices.
Required Technical Skills
5–8 years of professional experience in software engineering, algorithm development, scientific computing, data science, machine learning, or related technical disciplines.
Strong programming skills in Python with experience developing reliable and maintainable software.
Good experience with scientific computing and data analysis libraries such as NumPy, pandas, SciPy, or equivalent technologies.
Experience implementing data-processing, analytical, mathematical, or predictive algorithms.
Experience working with complex datasets and developing data-driven models, features, or analytical workflows.
Experience in one or more areas such as machine learning,…