Senior Data Scientist
Job Summary
Job Description
The Senior Data Scientist will analyze product performance in the fleet and provide support for data management activities within the Quality/Customer Service organization. This role involves collaborating with Engineering/Quality/CS teams and Information Technology. Key responsibilities include developing predictive maintenance models, translating concepts into engineered features, crafting custom features for enhanced model predictive power, and utilizing advanced technologies like Apache Spark, cloud-based machine learning platforms, and statistical methods. The role also encompasses optimizing code, deploying models into production, and creating insightful dashboards.
Must have:
- 6+ years of data science experience.
- Strong understanding of industrial processes and sensor data.
- Expertise in Apache Spark (PySpark, SparkSQL, SparkR) and distributed computing.
- Proficiency in Python, R, and SQL.
- Experience with cloud-based machine learning platforms (AWS SageMaker, Databricks).
16 skills required
Job Details
Enphase Energy is a global energy technology company and leading provider of solar, battery, and electric vehicle charging products. Founded in 2006, Enphase transformed the solar industry with our revolutionary microinverter technology, which turns sunlight into a safe, reliable, resilient, and scalable source of energy to power our lives. Today, the Enphase Energy System helps people make, use, save, and sell their own power. Enphase is also one of the fastest growing and innovative clean energy companies in the world, with approximately 68 million products installed across more than 145 countries.
We are building teams that are designing, developing, and manufacturing next-generation energy technologies and our work environment is fast-paced, fun, and full of exciting new projects.
If you are passionate about advancing a more sustainable future, this is the perfect time to join Enphase!
The Sr. Data Scientist will be responsible for analyzing product performance in the fleet. Provides support for the data management activities of the Quality/Customer Service organization. Collaborates with Engineering/Quality/CS teams and Information Technology.
What You Will Do
- Strong understanding of industrial processes, sensor data, and IoT platforms, essential for building effective predictive maintenance models.
- Experience translating theoretical concepts into engineered features, with a demonstrated ability to create features capturing important events or transitions within the data.
- Expertise in crafting custom features that highlight unique patterns specific to the dataset or problem, enhancing model predictive power. Ability to combine and synthesize information from multiple data sources to develop more informative features.
- Advanced knowledge in Apache Spark (PySpark, SparkSQL, SparkR) and distributed computing, demonstrated through efficient processing and analysis of large-scale datasets. Proficiency in Python, R, and SQL, with a proven track record of writing optimized and efficient Spark code for data processing and model training.
- Hands-on experience with cloud-based machine learning platforms such as AWS SageMaker and Databricks, showcasing scalable model development and deployment.
- Demonstrated capability to develop and implement custom statistical algorithms tailored to specific anomaly detection tasks.
- Proficiency in statistical methods for identifying patterns and trends in large datasets, essential for predictive maintenance. Demonstrated expertise in engineering features to highlight deviations or faults for early detection. Proven leadership in managing predictive maintenance projects from conception to deployment, with a successful track record of cross-functional team collaboration.
- Experience extracting temporal features, such as trends, seasonality, and lagged values, to improve model accuracy. Skills in filtering, smoothing, and transforming data for noise reduction and effective feature extraction.
- Experience optimizing code for performance in high-throughput, low-latency environments. Experience deploying models into production, with expertise in monitoring their performance and integrating them with CI/CD pipelines using AWS, Docker, or Kubernetes.
- Familiarity with end-to-end analytical architectures, including data lakes, data warehouses, and real-time processing systems.
- Experience creating insightful dashboards and reports using tools such as Power BI, Tableau, or custom visualization frameworks to effectively communicate model results to stakeholders.
- 6+ years of experience in data science with a significant focus on predictive maintenance and anomaly detection.
Who You Are and What you Bring
- Bachelor’s or Master’s degree/ Diploma in Engineering, Statistics, Mathematics or Computer Science
- 6+ years of experience as a Data Scientist
- Strong problem-solving skills
- Proven ability to work independently and accurately
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