Urwah Admin

Fusemachines

<div>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&rsquo; 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.
About the role:
This is a remote, full time consulting position (contract) responsible for designing, building, and maintaining the infrastructure required for data integration, storage, processing, and analytics (BI, visualization and Advanced Analytics) in the Media domain.
We are seeking a Senior Data Engineer with Snowflake, and proven implementation of clean rooms and data enrichment, delivering Data and Analytics products using Agile methodology. The ideal candidate will possess strong technical, analytical, and interpersonal skills.
Qualification / Skill Set Requirement:<ul><li>
5+ years of real-world data engineering development experience in Snowflake (certifications preferred)</li><li>
Proven experience as a Snowflake Developer, with a strong understanding of Snowflake architecture and concepts.</li><li>
Strong programming skills in SQL, with proficiency in writing efficient and optimized code for data integration, storage, processing, and manipulation.</li><li>
Proficient in snowflake services such as clean rooms, snowpipe, stages, stored procedures, views, materialized views, tasks and streams.</li><li>
Robust understanding of data partitioning and other optimization techniques in Snowflake.</li><li>
Knowledge of data security measures in Snowflake, including role-based access control (RBAC) and data encryption, and how those are applied to clean rooms.</li><li>
Highly skilled in one or more languages such as Python, Scala, and proficient in writing efficient and optimized code for data integration, storage, processing and manipulation.</li><li>
Proven experience in Azure.</li><li>
Strong knowledge of SDLC tools and technologies, including project management software (Jira or similar), source code management (GitHub or similar), CI/CD system (GitHub actions, AWS CodeBuild or similar) .</li><li>
Skilled in Data Integration from different sources such as APIs, databases, flat files, event streaming.</li><li>
Good understanding of Data Modeling and Database Design Principles. Being able to design and implement efficient database schemas that meet the requirements of the data architecture to support data solutions.</li><li>
Strong experience in working with ELT and ETL tools and being able to develop custom integration solutions as needed.</li><li>
Good understanding of Data Quality and Governance, including implementation of data quality checks and monitoring processes to ensure that data is accurate, complete, and consistent.</li><li>
Good Problem-Solving skills: being able to troubleshoot data processing pipelines and identify performance bottlenecks and other issues.</li><li>
Strong experience in working with ELT and ETL tools and being able to develop custom integration solutions as needed.</li><li>
Possesses strong leadership skills with a willingness to lead, create Ideas, and be assertive</li></ul>
Responsibilities:<ul><li>
Design and architect Snowflake data warehouses and databases to meet the data storage and processing requirements.</li><li>
Design and architect Snowflake clean rooms to meet the data security and sharing requirements.</li><li>
Create and optimize ELT (Extract, Load, Transform) processes to move, transform, and load data from various sources into Snowflake.</li><li>
Identify and resolve query performance bottlenecks and optimize SQL queries to ensure efficient data retrieval and processing.</li><li>
Integrate Snowflake with other data systems and tools, such as data lakes, analytics platforms, and BI tools.</li><li>
Collaborating with Product, Engineering, Data Scientists and Analysts to understand data requirements and develop data solutions, including reusable components.</li><li>
Evaluating and implementing new technologies and tools to improve data integration, data processing and analysis.</li><li>
Evaluate, design, and implement data governance solutions: cataloging, lineage, quality and data governance frameworks that are suitable for a modern analytics solution, considering industry-standard best practices and patterns.</li><li>
Assess best practices and design schemas that matches business needs for delivering a modern analytics solution (descriptive, diagnostic, predictive, prescriptive)</li><li>
Be an active member of our Agile team, participating in all ceremonies and continuous improvement activities.</li></ul>
Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.
3J9YuplTSv</div>

Sr Data Engineer - Snowflake

<div>
 
 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&rsquo; 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.
 
 
 
 About the role:
 
 
 
 This is a remote, full time consulting position (contract) responsible for designing, building, and maintaining the infrastructure required for data integration, storage, processing, and analytics (BI, visualization and Advanced Analytics) in the Media domain.
 
 
 We are seeking a Senior Data Engineer with Snowflake, and proven implementation of clean rooms and data enrichment, delivering Data and Analytics products using Agile methodology. The ideal candidate will possess strong technical, analytical, and interpersonal skills.
 
 
 
 Qualification / Skill Set Requirement:
 
 
 
 <ul>
 <li>
 5+ years of real-world data engineering development experience in Snowflake (certifications preferred)
 </li>
 <li>
 Proven experience as a Snowflake Developer, with a strong understanding of Snowflake architecture and concepts
 </li>
 <li>
 Strong programming skills in SQL, with proficiency in writing efficient and optimized code for data integration, storage, processing, and manipulation
 </li>
 <li>
 Proficient in snowflake services such as clean rooms, snowpipe, stages, stored procedures, views, materialized views, tasks and streams
 </li>
 <li>
 Robust understanding of data partitioning and other optimization techniques in Snowflake
 </li>
 <li>
 Knowledge of data security measures in Snowflake, including role-based access control (RBAC) and data encryption, and how those are applied to clean rooms
 </li>
 <li>
 Highly skilled in one or more languages such as Python, Scala, and proficient in writing efficient and optimized code for data integration, storage, processing and manipulation
 </li>
 <li>
 Proven experience in Azure
 </li>
 <li>
 Strong knowledge of SDLC tools and technologies, including project management software (Jira or similar), source code management (GitHub or similar), CI/CD system (GitHub actions, AWS CodeBuild or similar)
 </li>
 <li>
 Skilled in Data Integration from different sources such as APIs, databases, flat files, event streaming
 </li>
 <li>
 Good understanding of Data Modeling and Database Design Principles. Being able to design and implement efficient database schemas that meet the requirements of the data architecture to support data solutions
 </li>
 <li>
 Strong experience in working with ELT and ETL tools and being able to develop custom integration solutions as needed
 </li>
 <li>
 Good understanding of Data Quality and Governance, including implementation of data quality checks and monitoring processes to ensure that data is accurate, complete, and consistent
 </li>
 <li>
 Good Problem-Solving skills: being able to troubleshoot data processing pipelines and identify performance bottlenecks and other issues
 </li>
 <li>
 Strong experience in working with ELT and ETL tools and being able to develop custom integration solutions as needed
 </li>
 <li>
 Possesses strong leadership skills with a willingness to lead, create Ideas, and be assertive
 
 
 </li>
 </ul>
 
 Responsibilities:
 
 
 
 <ul>
 <li>
 Design and architect Snowflake data warehouses and databases to meet the data storage and processing requirements
 </li>
 <li>
 Design and architect Snowflake clean rooms to meet the data security and sharing requirements
 </li>
 <li>
 Create and optimize ELT (Extract, Load, Transform) processes to move, transform, and load data from various sources into Snowflake
 </li>
 <li>
 Identify and resolve query performance bottlenecks and optimize SQL queries to ensure efficient data retrieval and processing
 </li>
 <li>
 Integrate Snowflake with other data systems and tools, such as data lakes, analytics platforms, and BI tools
 </li>
 <li>
 Collaborating with Product, Engineering, Data Scientists and Analysts to understand data requirements and develop data solutions, including reusable components
 </li>
 <li>
 Evaluating and implementing new technologies and tools to improve data integration, data processing and analysis
 </li>
 <li>
 Evaluate, design, and implement data governance solutions: cataloging, lineage, quality and data governance frameworks that are suitable for a modern analytics solution, considering industry-standard best practices and patterns
 </li>
 <li>
 Assess best practices and design schemas that matches business needs for delivering a modern analytics solution (descriptive, diagnostic, predictive, prescriptive)
 </li>
 <li>
 Be an active member of our Agile team, participating in all ceremonies and continuous improvement activities
 
 
 </li>
 </ul>
 
 Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.
 
 
 
 Powered by JazzHR
 
 
 WvmQxNjWdc
</div>

<div>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&rsquo; 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.
About the role
This is a remote, full-time consulting position (contract), responsible for designing, building, and maintaining the infrastructure required for relationship-centric data integration, storage, and advanced analytics.
We are seeking an experienced Graph Data Engineer to implement Google Cloud&rsquo;s Unified Graph Fabric and strengthen a BigQuery-based &quot;Customer 360&quot; Knowledge Graph. The ideal candidate will move beyond traditional tabular reporting to analyze the &quot;web of influence&quot; within data, designing property graphs on BigQuery, implementing large-scale graph algorithms, and ensuring performant, cost-efficient graph queries, generating the corresponding pipelines that include AI.
Qualification &amp; Experience<ul><li>
Education: Must have a full-time Bachelor's degree in Computer Science, Data Engineering, or similar from a top-tier school.</li><li>
Experience: 8+ years of experience in data engineering roles, with 3+ years on Google Cloud Platform (GCP).</li><li>
Graph Expertise: Proven experience building production-grade data pipelines for use cases such as Identity Resolution, Knowledge Graph development, or Fraud Detection.</li><li>
Certifications: Google Cloud Professional Data Engineer certification is highly preferred.</li></ul>
Required skills/Competencies<ul><li>
Core GCP Data Engineering: 8+ years of real-world development experience with expert knowledge of BigQuery (including Dremel engine) and Cloud Spanner.</li><li>
Graph Languages &amp; SQL: Proficiency in GoogleSQL, including standard-compliant graph pattern matching (SQL/PGQ and ISO GQL), and writing complex relationship-traversal logic.</li><li>
Graph Architectures: Deep understanding of the trade-offs between Index-Free Adjacency (native graph stores) and Columnar Joins/Scans (BigQuery), and expertise in modeling using Adjacency Lists vs. Nested Fields.</li><li>
Python for Graphs: Expert knowledge in Python, specifically using BigQuery DataFrames (BigFrames), NetworkX, and GPU-accelerated backends like RAPIDS cuGraph.</li><li>
Orchestration &amp; CI/CD: Strong experience with dbt (preferred) and Prefect or Apache Airflow (Cloud Composer) for orchestrating complex task flows.</li><li>
Data Governance: Good understanding of data governance frameworks, including experience with Data Catalog and lineage tracking for graph features.</li></ul>
Responsibilities:<ul><li>
Graph Schema Implementation: Define and manage property graph schemas using declarative DDL (CREATE PROPERTY GRAPH) directly on existing relational tables and views in BigQuery and Cloud Spanner.</li><li>
Query Development &amp; Feature Engineering: Build reusable graph-query patterns (multi-hop traversal, neighborhood signals) and feature engineering pipelines for &quot;Customer 360&quot; analytics, utilizing SQL/GQL and the GRAPH_TABLE operator.</li><li>
Global Analytics Execution: Implement and operationalize key graph algorithms: including PageRank, Community Detection, Shortest Path, and Centrality, using BigQuery DataFrames and native dispatching mechanisms.</li><li>
Pipeline Automation: Design and schedule automated transformation pipelines (ELT) to prepare &quot;graph-ready&quot; data and move insights from BigQuery back to operational layers (e.g., Spanner Graph) via Reverse ETL.</li><li>
Performance Optimization: Optimize query execution to avoid &quot;referenced data&quot; billing traps by implementing advanced partitioning, clustering, and cost-management strategies.</li><li>
Collaboration: Work with Product, Engineering, and Data Science teams to understand data requirements and deliver graph-derived features for downstream machine learning models. 
 
Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws. 
</li></ul>
4NC3K474RW</div>

Senior Data Engineer

<div>
 
 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&rsquo; 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.
 
 
 
 Type: Full-time, Remote
 
 
 
 
 Role Overview
 
 
 
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;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
 
 
 
 <ul>
 <li>
 Problem Framing &amp; Stakeholder Partnership
 </li>
 <ul>
 <li>
 Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
 </li>
 <li>
 Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
 </li>
 </ul>
 <li>
 Data Analysis &amp; Feature Engineering
 </li>
 <ul>
 <li>
 Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
 </li>
 <li>
 Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
 </li>
 <li>
 Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
 </li>
 </ul>
 <li>
 Model Development (Core ML)
 </li>
 <ul>
 <li>
 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)
 </li>
 <li>
 Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
 </li>
 <li>
 Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
 </li>
 <li>
 Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
 </li>
 <li>
 Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
 </li>
 </ul>
 <li>
 Deep Learning
 </li>
 <ul>
 <li>
 Build and train deep learning models using PyTorch or TensorFlow/Keras
 </li>
 <li>
 Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
 </li>
 </ul>
 <li>
 Evaluation, Explainability, and Iteration
 </li>
 <ul>
 <li>
 Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
 </li>
 <li>
 Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
 </li>
 </ul>
 <li>
 Productionization &amp; MLOps (Project-Dependent)
 </li>
 <ul>
 <li>
 Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
 </li>
 <li>
 Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
 </li>
 </ul>
 <li>
 Documentation &amp; Communication
 </li>
 <ul>
 <li>
 Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
 </li>
 <li>
 Create documentation and lightweight demos that make results actionable
 
 </li>
 </ul>
 </ul>
 Success in This Role Looks Like
 
 
 <ul>
 <li>
 You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
 </li>
 <li>
 Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
 </li>
 <li>
 Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
 </li>
 <li>
 Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
 </li>
 <li>
 Strong SQL skills (joins, window functions, aggregation, performance awareness)
 </li>
 <li>
 Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
 </li>
 <li>
 Hands-on experience across multiple model types, including:
 </li>
 <ul>
 <li>
 Classification &amp; regression
 </li>
 <li>
 Time series forecasting
 </li>
 <li>
 Clustering/segmentation
 </li>
 </ul>
 <li>
 Experience with deep learning in PyTorch or TensorFlow/Keras
 </li>
 <li>
 Strong problem-solving skills: ability to work with ambiguous goals and messy data
 </li>
 <li>
 Clear communication skills and ability to translate analysis into decisions
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
 </li>
 <li>
 Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
 </li>
 <li>
 Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
 </li>
 <li>
 Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
 </li>
 <li>
 Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
 </li>
 <li>
 Consulting or client-facing delivery experience
 
 
 </li>
 </ul>
 
 Certifications (Strong Plus)
 
 
 
 Candidates with at least one relevant certification are especially encouraged to apply:
 
 
 <ul>
 <li>
 Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
 </li>
 <li>
 Databricks certifications (Data Scientist, Data Engineer, or related)
 
 
 </li>
 </ul>
 
 Nice-to-Have
 
 
 
 <ul>
 <li>
 Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
 </li>
 <li>
 Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
 </li>
 <li>
 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
 
 
 </li>
 </ul>
 
 Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
 
 
 
 Powered by JazzHR
 
 
 yJVdOHW6ou
</div>

Machine Learning Engineer / Data Scientist

<div>
 
 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&rsquo; 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.
 
 
 
 Type: Full-time, Remote
 
 
 
 
 Role Overview
 
 
 
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;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
 
 
 
 <ul>
 <li>
 Problem Framing &amp; Stakeholder Partnership
 </li>
 <ul>
 <li>
 Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
 </li>
 <li>
 Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
 </li>
 </ul>
 <li>
 Data Analysis &amp; Feature Engineering
 </li>
 <ul>
 <li>
 Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
 </li>
 <li>
 Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
 </li>
 <li>
 Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
 </li>
 </ul>
 <li>
 Model Development (Core ML)
 </li>
 <ul>
 <li>
 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)
 </li>
 <li>
 Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
 </li>
 <li>
 Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
 </li>
 <li>
 Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
 </li>
 <li>
 Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
 </li>
 </ul>
 <li>
 Deep Learning
 </li>
 <ul>
 <li>
 Build and train deep learning models using PyTorch or TensorFlow/Keras
 </li>
 <li>
 Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
 </li>
 </ul>
 <li>
 Evaluation, Explainability, and Iteration
 </li>
 <ul>
 <li>
 Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
 </li>
 <li>
 Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
 </li>
 </ul>
 <li>
 Productionization &amp; MLOps (Project-Dependent)
 </li>
 <ul>
 <li>
 Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
 </li>
 <li>
 Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
 </li>
 </ul>
 <li>
 Documentation &amp; Communication
 </li>
 <ul>
 <li>
 Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
 </li>
 <li>
 Create documentation and lightweight demos that make results actionable
 
 </li>
 </ul>
 </ul>
 Success in This Role Looks Like
 
 
 <ul>
 <li>
 You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
 </li>
 <li>
 Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
 </li>
 <li>
 Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
 </li>
 <li>
 Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
 </li>
 <li>
 Strong SQL skills (joins, window functions, aggregation, performance awareness)
 </li>
 <li>
 Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
 </li>
 <li>
 Hands-on experience across multiple model types, including:
 </li>
 <ul>
 <li>
 Classification &amp; regression
 </li>
 <li>
 Time series forecasting
 </li>
 <li>
 Clustering/segmentation
 </li>
 </ul>
 <li>
 Experience with deep learning in PyTorch or TensorFlow/Keras
 </li>
 <li>
 Strong problem-solving skills: ability to work with ambiguous goals and messy data
 </li>
 <li>
 Clear communication skills and ability to translate analysis into decisions
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
 </li>
 <li>
 Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
 </li>
 <li>
 Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
 </li>
 <li>
 Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
 </li>
 <li>
 Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
 </li>
 <li>
 Consulting or client-facing delivery experience
 
 
 </li>
 </ul>
 
 Certifications (Strong Plus)
 
 
 
 Candidates with at least one relevant certification are especially encouraged to apply:
 
 
 <ul>
 <li>
 Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
 </li>
 <li>
 Databricks certifications (Data Scientist, Data Engineer, or related)
 
 
 </li>
 </ul>
 
 Nice-to-Have
 
 
 
 <ul>
 <li>
 Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
 </li>
 <li>
 Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
 </li>
 <li>
 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
 
 
 </li>
 </ul>
 
 Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
 
 
 
 Powered by JazzHR
 
 
 hJ1yYBWaxu
</div>

Join Fusemachines and explore 5   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Fusemachines

About Company

Sr Data Engineer - Snowflake

Sr Data Engineer - Snowflake

Senior Data Engineer

Machine Learning Engineer / Data Scientist

Machine Learning Engineer / Data Scientist