Data Scientist

This is a remote position.About MonaireMonaire is building the infrastructure layer for intelligent commercial HVAC. We combine on-device sensors, smart thermostats, and machine-learning systems to automate control, surface real operational insight, and materially reduce energy waste at scale.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 hardwareLow-latency decision pipelines and control loopsML systems that survive missing data, drift, and adversarial real-world conditionsInfrastructure for model deployment, monitoring, and rollbackApps and services that customers depend on to run their buildings every dayThe 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 OverviewAs aData Scientist / Senior Data Scientist, you will play a critical role in buildingproduction-grade ML systemsthat drive real-world outcomes—energy efficiency, predictive maintenance, anomaly detection, and operational intelligence for HVAC/R systems.You will work closely withbackend engineers, product managers, and domain expertsto translate raw sensor data into reliable models that power customer-facing features and internal decision-making.This role requires someone who canthink long-term architecturally, while deliveringshort-term, measurable impactin a fast-moving startup environment.What You'll Do:Scale ML systems for 5X growth—optimize batch processing, database queries, and model inferenceDesign ML models for time-series data, anomaly detection, and predictive maintenanceOptimize production systems: <3s response times, 30% cost reduction, 99.9% uptimeDatabase optimization (MongoDB): indexes, connection pooling, 3-5X performance improvementBatch processing: parallel processing, async operations, memory managementModel optimization: <500ms inference latency, caching strategiesNLP & LLM: enhance conversational AI bots with intelligent query generationBuild monitoring systems: real-time dashboards, SLA tracking, automated scaling RequirementsMust-Have Skills2+ years hands-on data science/ML experienceStrong Python (NumPy, Pandas, Scikit-learn)Deep learning: TensorFlow, Keras, or PyTorchMongoDB: Query optimization, indexing, aggregation pipelinesDatabase optimization: Index design, query tuningBatch processing: Parallel processing (multiprocessing/async)Time-series data, anomaly detection, statistical modelingStrong CS fundamentals and debugging skillsNice-to-Have SkillsMLOps tools, Lambda optimization, caching (Redis/ElastiCache)Monitoring: Grafana, PrometheusNLP/LLM: Prompt engineering, conversational AIIoT/sensor data experience, startup experienceAWS: Lambda, S3, CloudWatch, ElastiCache/RedisDocker, SQL, Flask API developmentQualificationsBachelor's/Master's/PhD in CS, IT, Applied Math, Statistics, or related field BenefitsCompetitivesalary + equitywith meaningful ownershipComprehensivehealth insurance(self, spouse, children, and parents)Remote-first, flexible work cultureOpportunity to work onhigh-impact systems with climate and sustainability impactStrong emphasis onengineering excellence, ownership, and growth​Collaborative, inclusive, and low-ego team culture Originally posted on Himalayas