Highlights
Design predictive models, detect anomalies, scale ML pipelines, ensure actionable insights.
Description
Job Summary
pWe are seeking a Predictive Analytics Engineer to lead our transition from reactive repairs to proactive maintenance. This role involves developing predictive models, detecting anomalies in machine performance, and ensuring actionable insights for field engineers.
Responsibilities
- Design and deploy ML models to predict component failures and estimate Remaining Useful Life (RUL).
- Build robust algorithms for anomaly detection from high-frequency sensor data.
- Collaborate with the team to consume and refine data products from Snowflake and Databricks.
- Develop, test, and scale ML pipelines on Databricks/Snowflake.
- Own the end-to-end lifecycle of models—from experimentation in notebooks to production deployment using MLflow.
- Work with domain experts to ensure model outputs are actionable for field engineers.
- Support integration of predictive insights into GenAI-solutions for context-aware troubleshooting steps.
Required Skills
- Pandas
- Scikit-learn
- Databricks
- Anomaly Detection
- MLflow
Required Skills Explained
- Mastery of Python and SQL for data manipulation and analysis.
- Proficiency in PyTorch/TensorFlow for deep learning tasks.
- Proven experience with classical ML techniques such as XGBoost, Random Forest, and Scikit-learn on time-series or sensor data.
- Strong understanding of Time-Series Analysis, Survival Analysis, and Anomaly Detection methods (e.g., Isolation Forests, Autoencoders).
- Hands-on experience with Databricks/Spark for processing large-scale machine datasets.
- Familiarity with Survival Analysis or reliability engineering concepts to enhance predictive models.
- Experience with Deep Learning architectures like LSTMs or GRUs for sequential data analysis.
Who is this for
pThis role is ideal for a data scientist with experience in predictive analytics, ML development, and collaboration on big data platforms. You should have a passion for solving complex engineering problems and a drive to innovate.
Why This Job is a Good Opportunity
ulliTo be at the forefront of transforming service models from reactive to proactive, impacting global customer support directly.liA chance to work on cutting-edge technologies such as predictive analytics and machine learning in real-world applications.liOpportunities for collaboration with domain experts to ensure actionable insights for field engineers.liSupporting the integration of GenAI solutions to provide context-aware troubleshooting steps, enhancing customer service.liA dynamic environment that values innovation and continuous improvement, offering a platform for personal and professional growth.
Interview Preparation Tips
- Practice explaining your experience with Python, SQL, and ML frameworks like PyTorch/TensorFlow to a non-technical audience.
- Prepare examples of how you have used classical ML techniques (XGBoost, Random Forest) in real-world scenarios.
- Discuss specific projects where you implemented Time-Series Analysis or Anomaly Detection methods.
- Showcase your experience with Databricks/Spark for big data processing and explain its benefits over other tools.
- Briefly touch upon how you have integrated GenAI solutions into ML pipelines, if applicable.
Career Growth in This Role
pThis role offers significant opportunities for career advancement within the field of predictive analytics and machine learning. As a key driver in transitioning to proactive service models, you will gain valuable experience and expertise that can open doors to senior leadership positions or specialized roles such as ML engineering lead or data science manager.pThe job's focus on end-to-end ML pipeline development, model deployment, and monitoring also prepares you for emerging MLOps roles. Additionally, the collaboration with domain experts will enhance your problem-solving skills, making you a valuable asset to any team in need of data-driven solutions.
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Skills
Frequently Asked Questions
What experience is required for this role?Candidates should have hands-on experience in ML development, predictive modeling, and working with big data platforms like Databricks.
Is familiarity with GenAI/LLM integration necessary?While not mandatory, familiarity with integrating GenAI solutions is a plus as it enhances model outputs for troubleshooting.
What kind of data products will I be working with?You will work on and refine data products from Snowflake and Databricks to support predictive analytics and anomaly detection.
