▷ 3 Days Left: Machine Learning Engineer...

Role We're seeking a technically proficient and
commercially minded Machine Learning Engineer to join our Product
Listings team. The focus is on enabling smarter, scalable
advertising through automation and optimization. This hybrid role
combines deep data science expertise with production-grade ML
engineering. You will drive performance marketing automation by
designing experiments, building predictive models, and deploying
them at scale to optimize product bidding and maximize profits
across platforms like Google Shopping. You will work across the
entire ML lifecycle—from exploring datasets and engineering
features to evaluating models and running validation experiments.
Additionally, you will build, deploy, and monitor models in
production, setting up retraining workflows, pipeline
orchestration, and performance alerts, supported by your tech lead
and colleagues from the Data Science chapter at Lyst. Our main
tools include Python with frameworks like SKLearn, TensorFlow, and
PyTorch, operating within AWS Sagemaker where possible. We
emphasize clean, documented, well-tested, and reviewed code,
supported by our tooling and culture. This role offers a hands-on,
high-impact opportunity that blends research, experimentation, and
engineering, all tied to clear business outcomes. You will
collaborate closely with marketers, data analysts, and engineers to
shape the future of scalable advertising. Responsibilities -
Productionize ML models into robust, maintainable systems,
including retraining pipelines, monitoring, and performance alerts.

• Build and evaluate ML models supporting smarter paid marketing,
  such as ROAS prediction, lifecycle-aware bidding, and grouping
  optimization. - Explore large-scale datasets to uncover insights,
  test hypotheses, and identify features that drive business value. -
  Handle all aspects of the data science workflow—from data cleaning
  and analysis to offline benchmarking and validation. - Collaborate
  with marketers to reduce manual campaign management overhead while
  improving ad efficiency and profitability. - Enhance our ad feed
  infrastructure to optimize product attributes, pricing, and
  availability for campaign performance. - Contribute to product data
  initiatives, especially where they impact ad quality or model
  inputs. Minimum Requirements - 3+ years of experience building and
  deploying ML models in production. - Strong Python and SQL skills,
  with expertise in data wrangling, feature engineering, and model
  evaluation. - Deep understanding of the data science process,
  including exploratory analysis, statistical testing, and model
  comparison. - Experience with structured prediction problems using
  real-world, messy data. - Familiarity with advertising systems
  (Google Shopping, PMAX) and marketing metrics like ROAS, profit,
  and conversion is advantageous. - Experience in productionizing ML
  models, including training pipelines, versioning, monitoring, and
  retraining. We also offer a flexible working environment,
  comprehensive benefits, and a commitment to diversity and
  inclusion. We encourage candidates from all backgrounds to apply,
  even if they don't meet every requirement.
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