{"id":1722558,"url":"https://alion.io/job/monaire-data-scientist","title":"Data Scientist","company":{"id":3781369,"name":"Monaire","domain":"monaire.ai","url":"https://alion.io/company/monaire","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":62000,"max_usd":154000,"period":"year","method":"global_role_seniority_cell","sample_n":525},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Keras","optional":false},{"name":"LLM","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Docker","optional":true},{"name":"Flask","optional":true},{"name":"Grafana","optional":true},{"name":"Prometheus","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Redis","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-10-02T19:02:15Z","employer_posted_date":null,"last_verified_at":"2026-10-02T19:02:15Z","board_verified":false,"closed_at":null,"days_open":9,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":9},"description":"This is a remote position.About Monaire\nMonaire 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. \n\nEngineers here work on: \n\nData ingestion and streaming at scale from heterogeneous hardware \n\nLow-latency decision pipelines and control loops \n\nML systems that survive missing data, drift, and adversarial real-world conditions \n\nInfrastructure for model deployment, monitoring, and rollback \n\nApps and services that customers depend on to run their buildings every day \n\nThe 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. \n\nRole Overview\nAs 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.\n\nYou 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.\n\nThis role requires someone who canthink long-term architecturally, while deliveringshort-term, measurable impactin a fast-moving startup environment.\n\nWhat You'll Do:\n\nScale ML systems for 5X growth—optimize batch processing, database queries, and model inference\n\nDesign ML models for time-series data, anomaly detection, and predictive maintenance\n\nOptimize production systems: <3s response times, 30% cost reduction, 99.9% uptime\n\nDatabase optimization (MongoDB): indexes, connection pooling, 3-5X performance improvement\n\nBatch processing: parallel processing, async operations, memory management\n\nModel optimization: <500ms inference latency, caching strategies\n\nNLP & LLM: enhance conversational AI bots with intelligent query generation\n\nBuild monitoring systems: real-time dashboards, SLA tracking, automated scaling\n\nRequirements\n\nMust-Have Skills\n2+ years hands-on data science/ML experience\n\nStrong Python (NumPy, Pandas, Scikit-learn)\n\nDeep learning: TensorFlow, Keras, or PyTorch\n\nMongoDB: Query optimization, indexing, aggregation pipelines\n\nDatabase optimization: Index design, query tuning\n\nBatch processing: Parallel processing (multiprocessing/async)\n\nTime-series data, anomaly detection, statistical modeling\n\nStrong CS fundamentals and debugging skills\n\nNice-to-Have Skills\nMLOps tools, Lambda optimization, caching (Redis/ElastiCache)\n\nMonitoring: Grafana, Prometheus\n\nNLP/LLM: Prompt engineering, conversational AI\n\nIoT/sensor data experience, startup experience\n\nAWS: Lambda, S3, CloudWatch, ElastiCache/Redis\n\nDocker, SQL, Flask API development\n\nQualifications\nBachelor's/Master's/PhD in CS, IT, Applied Math, Statistics, or related field\n\nBenefits\n\nCompetitivesalary + equitywith meaningful ownership\n\nComprehensivehealth insurance(self, spouse, children, and parents)\n\nRemote-first, flexible work culture\n\nOpportunity to work onhigh-impact systems with climate and sustainability impact\n\nStrong emphasis onengineering excellence, ownership, and growth\n\nCollaborative, inclusive, and low-ego team culture\n\nOriginally posted on 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