Machine Learning Engineer / Data Scientist

Summary

Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. They are seeking a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact, working across the ML lifecycle in collaboration with client stakeholders and internal teams.

Responsibilities

• 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)
• 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
• 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
• 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)
• 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
• 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
• Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
• Create documentation and lightweight demos that make results actionable

Skills

• 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
• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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