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Lead ML Engineer

Markmonitor

Key Facts

Remote From: 
Canada
Full time
English

Hard Skills

SQL (Programming Language) Python (Programming Language) Machine Learning Data Engineering Data Lineage Observability Analytical Dashboard A/B Testing Data Warehouse Architectures Databricks Oracle Exadata Data Warehouse Architectures Data Architecture Pandas (Python Package) Controlled Experiments BigQuery Snowflake (Data Warehouse) Tableau (Business Intelligence Software) Design Documentation Software Technical Review Endpoint Management Machine Learning Machine Learning Feature Engineering Build Pipeline Data Quality Assessment Back End Testing Scikit-Learn (Python Package) LightGBM Power BI Ethical AI Data Collection PyTorch (Machine Learning Library) Apache Spark Azure Machine Learning Xgboost AWS SageMaker Amazon Redshift TensorFlow Batch Processing Hugging Face (NLP Framework) Controlled Experiments Stakeholder Communications Requirements Engineering KPI Management Technical Writing

Other Skills

  • •
    Communication
  • •
    Leadership
  • •
    Teamwork
  • •
    Mentorship

Roles & Responsibilities

  • 8+ years in applied ML and data engineering, including 3+ years leading production ML deployments.
  • Python expert with production-grade SQL; strong in pandas/Polars, scikit-learn, and one of XGBoost or LightGBM.
  • Fluency with core ML toolkits (TensorFlow, PyTorch, scikit-learn) and familiarity with Hugging Face or similar frameworks.
  • Proven ability to build and maintain scalable batch and streaming data pipelines; hands-on with Oracle Exadata, Spark/Databricks, Snowflake, BigQuery, or Redshift; orchestration (Airflow/Prefect/Dagster); cloud ML platforms (SageMaker/Vertex/Azure ML); and MLOps (CI/CD, experiment tracking, model registry, observability).

Requirements:

  • Partner with the business to translate requirements into problem statements, KPIs, and experiment plans (A/B, holdout, backtests).
  • Design data/ML architectures on lakehouse/warehouse stacks and build ingestion, feature engineering, and training pipelines (batch and streaming) using Python/SQL with orchestration (Airflow/Prefect/Dagster).
  • Develop and productionize models using scikit-learn/XGBoost/LightGBM and PyTorch/TensorFlow; manage experiments, lineage, and model serving on a major cloud ML platform with CI/CD, canaries/blue-green, and rollback guardrails.
  • Monitor data/model quality and drift; implement alerting and dashboards; collaborate with BI; document designs, mentor engineers, and champion responsible AI practices.

Job description

About Us:

Newfold Digital is a leading web technology company serving millions of customers globally. Our customers know us through our robust portfolio of brands. We have some of the industry's most prominent and storied go-to-market brands, including Bluehost, HostGator, Network Solutions, Domain.com, Register.com, and Web.com. We help customers of all sizes build a digital presence that delivers results. With our extensive product offerings and personalized support, we take pride in collaborating with our customers to serve their online presence needs. The strength of our company lives in the intersection of our people, our customers, and our brands. 

What you'll do & how you'll make your mark:

  • Partner with the business to translate requirements into clear problem statements, KPIs, and experiment plans (A/B, holdout, backtests). 
  • Design data & ML architectures on lakehouse/warehouse stacks (e.g., Oracle Exadata, Spark/Databricks;  Snowflake/BigQuery/Redshift with open table formats like Iceberg/Delta/Hudi or equivalent ).  
  • Build pipelines for ingestion, feature engineering, and training (batch & streaming) using Python + SQL with orchestration (Airflow/Prefect/Dagster). 
  • Model using scikit-learn/XGBoost/LightGBM and  PyTorch/TensorFlow; manage experiments and lineage  
  • Serve & operate models on a major cloud ML platform (Azure ML, SageMaker or Vertex AI), with CI/CD, canary/blue-green, and rollback guardrails.  
  • Monitor & improve: implement data/model quality and drift monitoring, alerting, and dashboards; close the loop with BI (Power BI/Tableau/Looker).  
  • Document & review: author concise design docs and run technical reviews; mentor engineers; champion responsible AI practices. 

Who you are & how you'll make your mark:

  • 8+ years in applied ML & data engineering (3+ years leading delivery of production ML systems).
  • Python expert with production-grade SQL; strong with pandas/Polars, scikit-learn, and one of: XGBoost/LightGBM.
  • Fluency in core ML toolkits including TensorFlow, PyTorch, scikit-learn, and familiarity with Hugging Face or equivalent frameworks.
  • Proven record of constructing and maintaining scalable data pipelines—both batch and streaming—for model training and deployment.
  • Data platforms: hands-on with one of: Oracle ExaData, Spark/Databricks, or Snowflake, BigQuery/Redshift or equivalent; comfortable with open table formats (Iceberg/Delta/Hudi).
  • Orchestration: real projects using one of Airflow, Prefect, or Dagster.
  • Cloud ML platform: production deployments on one of SageMaker, Vertex AI, or Azure ML (pipelines, endpoints, registries).
  • MLOps: CI/CD for ML, experiment tracking, model registry, observability (latency, errors), and data/model drift monitoring.
  • Communication: ability to frame trade-offs and influence cross-functional partners; crisp writing of design/decision docs.


This Job Description includes the essential job functions required to perform the job described above, as well as additional duties and responsibilities. This Job Description is not an exhaustive list of all functions that the employee performing this job may be required to perform. The Company reserves the right to revise the Job Description at any time, and to require the employee to perform functions in addition to those listed above.

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