Data Scientist

About Monaire

This is not offline modeling or notebook ML. Models run in production, interact with physical systems, and must be observable, debuggable, and correct. The platform spans edge devices, cloud services, streaming pipelines, control logic, and ML inference.

Engineers here work on:

• Data ingestion and streaming at scale from heterogeneous hardware
  

• Low-latency decision pipelines and control loops
  

• ML systems that survive missing data, drift, and adversarial real-world conditions
  

• Infrastructure for model deployment, monitoring, and rollback
  

• Apps and services that customers depend on to run their buildings every day
  

The market is large, broken, and technically underserved. We’re scaling the system and need engineers who care about correctness, performance, and ownership — people who want to build infrastructure that actually controls the physical world, not just dashboards that look good in demos.

Role Overview

As a Data Scientist / Senior Data Scientist, you will play a critical role in building production-grade ML systems that drive real-world outcomes—energy efficiency, predictive maintenance, anomaly detection, and operational intelligence for HVAC/R systems.

You will work closely with backend engineers, product managers, and domain experts to translate raw sensor data into reliable models that power customer-facing features and internal decision-making.

This role requires someone who can think long-term architecturally, while delivering short-term, measurable impact in a fast-moving startup environment.

What You'll Do:

• Scale ML systems for 5X growth—optimize batch processing, database queries, and model inference
  

• Design ML models for time-series data, anomaly detection, and predictive maintenance
  

• Optimize production systems: <3s response times, 30% cost reduction, 99.9% uptime
  

• Database optimization (MongoDB): indexes, connection pooling, 3-5X performance improvement
  

• Batch processing: parallel processing, async operations, memory management
  

• Model optimization: <500ms inference latency, caching strategies
  

• NLP & LLM: enhance conversational AI bots with intelligent query generation
  

• Build monitoring systems: real-time dashboards, SLA tracking, automated scaling
  

Requirements

Must-Have Skills

• 2+ years hands-on data science/ML experience
  

• Strong Python (NumPy, Pandas, Scikit-learn)
  

• Deep learning: TensorFlow, Keras, or PyTorch
  

• MongoDB: Query optimization, indexing, aggregation pipelines
  

• Database optimization: Index design, query tuning
  

• Batch processing: Parallel processing (multiprocessing/async)
  

• Time-series data, anomaly detection, statistical modeling
  

• Strong CS fundamentals and debugging skills
  

Nice-to-Have Skills

• MLOps tools, Lambda optimization, caching (Redis/ElastiCache)
  

• Monitoring: Grafana, Prometheus
  

• NLP/LLM: Prompt engineering, conversational AI
  

• IoT/sensor data experience, startup experience
  

• AWS: Lambda, S3, CloudWatch, ElastiCache/Redis
  

• Docker, SQL, Flask API development
  

Qualifications

Bachelor's/Master's/PhD in CS, IT, Applied Math, Statistics, or related field

Benefits

• Competitive salary + equity with meaningful ownership
  

• Comprehensive health insurance (self, spouse, children, and parents)
  

• Remote-first, flexible work culture
  

• Opportunity to work on high-impact systems with climate and sustainability impact
  

• Strong emphasis on engineering excellence, ownership, and growth
  

• ​Collaborative, inclusive, and low-ego team culture