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A senior ML role at Hyper AdTech, a large Russian programmatic (mobile in-app RTB) advertising platform. Day to day the person builds and productionizes models for click/conversion prediction, bidding, budget pacing and traffic-quality scoring over large bidstream/event data using Python and classic ML tooling.
Internal Coupang transfer role: a Staff ML Engineer on the Coupang Media Group Offsite Ads team in Seattle designs and ships machine learning systems for ad targeting, ranking, bidding, and CTR/CVR prediction, using Python/Java, PyTorch, LLMs, and large-scale ML infrastructure.
Staff Data Scientist at HelloFresh in Chippendale, Sydney (hybrid) leads and grows a data science team, translating business challenges into ML models, experimentation frameworks, and analytics that drive commercial outcomes. Day-to-day blends people leadership with hands-on Python, PySpark, Databricks, XGBoost, and A/B testing work.
12-month contract data engineer role at NTT Singapore (Alexandra Building, Singapore), designing and operating enterprise Lakehouse platforms and batch/streaming pipelines with Databricks, Apache Spark, PySpark, Python and SQL, using Delta Lake/Iceberg/Hudi, Kafka, Airflow and Docker/Kubernetes CI/CD to support analytics, AI and GenAI use cases.
Director of Data Science at Known, a marketing analytics and creative agency, leading client engagements that blend data science, AI, and marketing strategy from scoping through delivery, while managing and mentoring a team of analysts and data scientists. Core stack: Python, SQL, Git, machine learning (linear/XGBoost), statistics, and experimentation.
Data Scientist III on Inter's Growth/CRM data science team in Belo Horizonte, building statistical analyses, end-to-end machine learning models, and experiments to deliver hyper-personalized customer experiences for Brazil's first digital bank's financial super app. Core stack: Python, SQL, Apache Spark.
Build and deploy large-scale ML systems for recommendations, search, ads, and LLM-driven features that power Reddit’s community platform.
Build and deploy large-scale ML systems for personalized recommendations, search, and ads at Reddit, using PyTorch/TensorFlow and distributed pipelines.
Build meaningful data products and apply Machine Learning using Python, SQL, and AWS. The role involves data modeling and communicating insights to clients.
Builds and maintains credit risk/repayment models for Clair’s fintech platform, using ML to assess risk, predict user behavior, and optimize credit decisions. Works with transaction data, deploys models in production (AWS/Snowflake), and collaborates with product/risk teams.
An Actuarial Data Scientist at Hippo leads loss cost modeling for the Homeowners insurance program — designing and deploying predictive frequency/severity models and building Python- and SQL-based analytics pipelines (Airflow-orchestrated), while partnering with pricing, underwriting, and engineering.
Build and deploy AI/ML models and GenAI assistants using Python, LLMs, and vector databases; develop APIs and frontend interfaces to integrate AI solutions into products.
Lead a team of ML engineers and architects at an AI-first cloud services company, owning hiring, team growth, and complex customer engagements while shaping AI/ML architectures and driving pre-sales.
Build and deploy ML models to detect account-sharing fraud in real time for a global anime streaming platform, using anomaly detection and cloud-based MLOps pipelines.
Remote (Mexico-based) Data Scientist role at staffing firm Cobalto Talent, supporting U.S.-based clients: cleaning/analyzing large datasets, building and deploying ML models, running A/B tests and forecasting, and communicating insights to stakeholders. Core stack: Python, SQL, ML libraries (TensorFlow, PyTorch, XGBoost), cloud platforms (AWS/Azure/GCP), and BI tools (Power BI/Tableau/Looker).
Design and deploy AI models that process high-frequency sensor data from IoT devices to enable real-time automation in physical infrastructure systems.
Remote part-time (20h/week, path to full-time) ML Engineer at Analytix: build churn, LTV, VIP-potential and responsible-gaming models for a B2B iGaming analytics platform. Work covers ClickHouse feature engineering, reproducible training and batch-scoring pipelines, MLflow tracking, SHAP explainability, and drift monitoring using Python, scikit-learn and gradient boosting.
Designs, trains, tests, and deploys large-scale distributed machine learning systems, working in an agile team with data scientists and engineers to integrate ML feedback loops into products. Key technologies include Python/Golang/C++, ML libraries like TensorFlow and scikit-learn, Docker/Kubernetes, and cloud platforms.
Builds and scales AI-driven data pipelines for Shopify brands, integrating e-commerce platforms and automating inventory forecasting with Python, SQL, and AI tools.
Designs, deploys, and maintains AI/ML models and MLOps pipelines for federal government clients, primarily using Python, TensorFlow/PyTorch, and cloud platforms like AWS/Azure/GCP.
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