Data Scientist

 

About Fusemachines
Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail,  manufacturing, and government.

Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.

Salary Range: US$ 140,000-190,000/year

Role Overview

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.

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.

Key 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 recommendat