Fusemachines

Machine Learning Engineer / Data Scientist

Fusemachines

New York•On-site•Salary•3-6 Yrs•Jun 30

Job description

Overview

About Fusemachines Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI.

Responsibilities

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.
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.

Qualifications

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
Strong problem-solving skills: ability to work with ambiguous goals and messy data.
Clear communication skills and ability to translate analysis into decisions.
Consulting or client-facing delivery experience.
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.
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).

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