Role description
As our first Senior Data Scientist, you will build the models and analytical foundations that turn raw industrial telemetry into customer-facing insights, alerts, and predictions. While our software engineering team builds the high-scale infrastructure and data pipelines, your mission is to transform that raw telemetry into the "brain" of our product. You will define the requirements for how data should be structured for analysis, develop the models that identify industrial inefficiencies, and lead the charge in turning "noise" into high-value customer insights. You will help define the analytical data requirements and partner closely with engineering on how data should be structured and exposed for modeling.
Technical Requirements
1. Statistical Modeling & Analysis
- 6-8+ Years of Data Science: Proven experience building predictive models and extracting actionable signals from massive, high-velocity datasets.
- Time-Series Mastery (Hard Requirement): Deep mathematical understanding of signal processing, anomaly detection, and forecasting. You must have extensive hands-on experience manipulating and analyzing time-series data (IoT and sensor data experience preferred, but we are open to time-series experts from other high-volume domains).
- Advanced SQL: Expert-level skills to explore "Big Data" scales. You can navigate partitioned datasets in Snowflake, BigQuery, or Athena to extract features and validate hypotheses.
2. The Data Science Stack
- Python Mastery: Expert proficiency in the standard DS stack (Pandas, NumPy, Scikit-learn, SciPy) and visualization libraries.
- MLOps Mindset: Experience taking models from a notebook into a production environment, ensuring they are reproducible, testable, and scalable.
- Cloud & Data Platform Fluency: Working knowledge of modern cloud data tooling and infrastructure so you can effectively partner with engineers on how analytical data should be stored, transformed, and served.
3. Analytics Engineering & Strategy
- The "Logic" Layer: You will define the business logic for materialized views and "clean" data schemas that the UI Engineers will use to power customer-facing charts.
- Experimental Design: Ability to design frameworks for validating data accuracy and model performance in real-world industrial settings.
Key Responsibilities
1. Advanced Analytics & Modeling
- Predictive Insights: Develop models to predict equipment failure, energy spikes, or operational bottlenecks for our industrial clients.
- Signal Extraction: Apply statistical techniques to filter out sensor noise and identify the true "events" that matter to a facility manager.
2. Data Strategy & Engineering Partnership
- Infrastructure Stakeholder: Act as the "internal customer" for the Software Engineering team. You will define the schemas, latency requirements, and data "landing zones" you need to perform high-level analysis.
- The Feedback Loop: Identify "blind spots" in our current telemetry. You will tell the Backend Engineers exactly what data points we are missing to make our AI and BI layers more effective.
3. Product & UI Collaboration
- BI & Visualization Logic: Partner with UI Engineers to ensure dashboards surface meaningful trends and actionable intelligence, rather than just plotting raw telemetry.
- Feature Translation: Work closely with the Product team to translate complex predictive models (e.g., anomaly scores, failure probabilities) into intuitive, customer-facing features, alerts, and reports.
- Model Refinement: Incorporate user feedback from the UI directly into your models. Refine model sensitivity and alert thresholds based on customer feedback, investigation outcomes, and observed false-positive/false-negative patterns.
Nice-to-Haves (Training Provided)
- Industrial Domain Knowledge: Familiarity with how data is generated by LoRaWAN, BACnet, or Modbus devices.
- Deep Learning: Experience with neural networks for complex pattern recognition in time-series data.
- Domain Expertise: Prior experience in IoT, Green-tech, Prop-tech, or Manufacturing.
The Profile
You are a "data detective" who is more interested in what the data means than how the database is sharded. You are a proactive communicator who can tell an engineer, "I need this data partitioned by device-type for my model to run efficiently," and tell a client, "Here is the statistical probability that your HVAC system will fail in the next 30 days." You are looking for a high-impact role where you can define the analytical foundation of a scaling company.
