RandomTrees
Technical Recruitment | Talent Acquisition Specialist | Strategic Hiring | Vendor Management | End to End Recruitment
Key Responsibilities
Design and productionize models for opportunity scanning, anomaly detection, and significant change detection across CRM, streaming, ecommerce, and social data.
Define and tune alerting logic (thresholds, SLOs, precision/recall) to minimize noise while surfacing high-value marketing actions.
Partner with marketing, product, and data engineering to operationalize insights into campaigns, playbooks, and automated workflows, with clear monitoring and experimentation.
Required Qualifications
Strong proficiency in
Python
(pandas, NumPy, scikit-learn; plus experience with PySpark or similar for large-scale data) and
SQL
on modern warehouses (e.g., BigQuery, Snowflake, Redshift).
Hands-on experience with
time-series modeling
and
anomaly / changepoint / significant‑movement detection
(e.g., STL decomposition, EWMA/CUSUM, Bayesian/prophet‑style models, isolation forests, robust statistics).
Experience building and deploying
production ML pipelines
(batch and/or streaming), including feature engineering, model training, CI/CD, and monitoring for performance and data drift.
Solid background in
statistics and experimentation : hypothesis testing, power analysis, A/B testing frameworks, uplift/propensity modeling, and basic causal inference techniques.
Familiarity with
cloud platforms
(GCP/AWS/Azure), orchestration tools (e.g., Airflow/Prefect), and
dashboarding/visualization
tools to expose alerts and model outputs to business users.
Seniority level Mid-Senior level
Employment type Contract
Job function Information Technology
Industries IT Services and IT Consulting
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Key Responsibilities
Design and productionize models for opportunity scanning, anomaly detection, and significant change detection across CRM, streaming, ecommerce, and social data.
Define and tune alerting logic (thresholds, SLOs, precision/recall) to minimize noise while surfacing high-value marketing actions.
Partner with marketing, product, and data engineering to operationalize insights into campaigns, playbooks, and automated workflows, with clear monitoring and experimentation.
Required Qualifications
Strong proficiency in
Python
(pandas, NumPy, scikit-learn; plus experience with PySpark or similar for large-scale data) and
SQL
on modern warehouses (e.g., BigQuery, Snowflake, Redshift).
Hands-on experience with
time-series modeling
and
anomaly / changepoint / significant‑movement detection
(e.g., STL decomposition, EWMA/CUSUM, Bayesian/prophet‑style models, isolation forests, robust statistics).
Experience building and deploying
production ML pipelines
(batch and/or streaming), including feature engineering, model training, CI/CD, and monitoring for performance and data drift.
Solid background in
statistics and experimentation : hypothesis testing, power analysis, A/B testing frameworks, uplift/propensity modeling, and basic causal inference techniques.
Familiarity with
cloud platforms
(GCP/AWS/Azure), orchestration tools (e.g., Airflow/Prefect), and
dashboarding/visualization
tools to expose alerts and model outputs to business users.
Seniority level Mid-Senior level
Employment type Contract
Job function Information Technology
Industries IT Services and IT Consulting
#J-18808-Ljbffr