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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer / Data Scientist - **Company:** Fusemachines Inc. - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Artificial Neural Networks, Automation of Tests, Microsoft Azure, BigQuery, Cloud Computing, Cluster Analysis, Encodings, Data Profiling, Data Warehousing, Relational Databases, Database Queries, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Tensorflow, SQL Databases, Management of Software Versions, Supervised Learning, Google Cloud, Feature Engineering, Pytorch, Snowflake, Apache Spark, Deep Learning, Keras, Gaussian, Pandas, Build Management, Containerization, Data Lakes, Core Data, Scikit Learn, Data Lineage, Data Analytics, Xgboost, Machine Learning Operations, Code Restructuring, K Means, Docker, Databricks - **Published:** July 27, 2026 - **Apply:** https://www.juju.com/job/00000000gjyyso ## About the Role You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems., + 3-8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior). + Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent). + Strong SQL skills (joins, window functions, aggregation, performance awareness). + Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset. + Hands-on experience across multiple model types, including: + Classification & regression + Time series forecasting + Clustering/segmentation + Experience with deep learning in PyTorch or TensorFlow/Keras. + Strong problem-solving skills: ability to work with ambiguous goals and messy data. + Clear communication skills and ability to translate analysis into decisions. Preferred Qualifications + Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows). + Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining). + Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks). + Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker). + Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls). + Consulting or client-facing delivery experience. Certifications (Strong Plus) Candidates with at least one relevant certification are especially encouraged to apply: + Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks) + Databricks certifications (Data Scientist, Data Engineer, or related) Nice-to-Have + Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests). + Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products. + Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability. ## Description We're hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You'll work across the ML lifecycle-from problem framing and data exploration to model development, evaluation, deployment, and monitoring-often in partnership with client stakeholders and internal delivery teams., + Problem Framing & Stakeholder Partnership + Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.). + Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability). + Data Analysis & Feature Engineering + Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses. + Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices. + Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions. + Model Development (Core ML) + Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data). + Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation. + Build time series models (statistical and ML/DL approaches) and validate with proper backtesting. + Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness. + Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making. + Deep Learning + Build and train deep learning models using PyTorch or TensorFlow/Keras. + Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design). + Evaluation, Explainability, and Iteration + Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports. + Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence. + Productionization & MLOps (Project-Dependent) + Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration. + Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans. + Documentation & Communication + Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders. + Create documentation and lightweight demos that make results actionable. Success in This Role Looks Like + You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency). + Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring. + Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly., Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including: + Direct Company Sponsorship: Such as H-1B, J-1, or TN visas. + Employer of Record: Listing Fusemachines as the immigration employer on any government documentation. + Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT). ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Vectorize all the things! 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