AgileEngine
Senior Data Scientist – MMM ID71005
ZONA 1, GT现场永久制全职
发布于 2026年8月28日
此职位以 ES 发布
AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a Senior Data Scientist to drive rigorous statistical execution, multi-goal tuning, and algorithmic validation of client-specific Meridian MMM frameworks, translating noisy marketing signals and low-resolution data matrices into trusted, multi-objective spend recommendations. You will formulate heuristic baseline models for high-variance datasets, ingest structured data from upstream pipelines for client-isolated experimentation, and apply Python with advanced SQL against AWS Athena and PostgreSQL environments.
WHAT YOU WILL DO
- Expand framework configurations to natively factor in multi-layered client objectives, such as separating mid-funnel brand awareness metrics from immediate revenue generation targets.
- Formulate robust heuristic models and pragmatic backup strategies to generate valid analytical insights when working with limited or high-variance customer datasets.
- Safely ingest highly structured fields derived from upstream single source of truth data pipelines to execute client-isolated experimentation.
- Leverage modern AI-assisted IDE tools to expedite data transformations while applying strict analytical validation to catch tool errors or structural flaws before deployment.
- Collaborate daily with internal engineering cross-functions via Slack and Google Meet to rapidly resolve ambiguous pipeline requirements or technical dependencies.
MUST HAVES
- 5–7 years of professional data science experience, emphasizing time-series analysis, prior distributions, or high-complexity attribution modeling.
- Clear, verifiable experience applying or testing the Meridian MMM framework on real-world datasets.
- Strong backend proficiency using Python, specifically leveraging tools that reinforce static type systems and structured schemas.
- Absolute comfort operating entirely inside standard Mac/Linux terminal infrastructure using Bash, shell utilities, and text-based tools.
- Proficiency writing advanced queries to extract and prepare data housed in cloud systems such as AWS Athena or PostgreSQL environments.
- Strong communication skills, specifically the assertiveness to defend a technical or mathematical methodology against a project shortcut that threatens calculation validity.
- Upper-intermediate English level.
NICE TO HAVES
- Familiarity with containerized applications managed under Docker or basic Kubernetes infrastructure paradigms.
- Practical knowledge of structural time-series models, Bayesian inference techniques, or custom Markov Chain Monte Carlo (MCMC) configurations.
- Prior experience dealing with multi-account privacy boundaries and strict compliance mandates regarding client data partitioning.
- Background in analyzing digital platform performance APIs, such as Google Ads, Amazon Advertising, or YouTube Data systems.
PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a Senior Data Scientist to drive rigorous statistical execution, multi-goal tuning, and algorithmic validation of client-specific Meridian MMM frameworks, translating noisy marketing signals and low-resolution data matrices into trusted, multi-objective spend recommendations. You will formulate heuristic baseline models for high-variance datasets, ingest structured data from upstream pipelines for client-isolated experimentation, and apply Python with advanced SQL against AWS Athena and PostgreSQL environments.
WHAT YOU WILL DO
- Expand framework configurations to natively factor in multi-layered client objectives, such as separating mid-funnel brand awareness metrics from immediate revenue generation targets.
- Formulate robust heuristic models and pragmatic backup strategies to generate valid analytical insights when working with limited or high-variance customer datasets.
- Safely ingest highly structured fields derived from upstream single source of truth data pipelines to execute client-isolated experimentation.
- Leverage modern AI-assisted IDE tools to expedite data transformations while applying strict analytical validation to catch tool errors or structural flaws before deployment.
- Collaborate daily with internal engineering cross-functions via Slack and Google Meet to rapidly resolve ambiguous pipeline requirements or technical dependencies.
MUST HAVES
- 5–7 years of professional data science experience, emphasizing time-series analysis, prior distributions, or high-complexity attribution modeling.
- Clear, verifiable experience applying or testing the Meridian MMM framework on real-world datasets.
- Strong backend proficiency using Python, specifically leveraging tools that reinforce static type systems and structured schemas.
- Absolute comfort operating entirely inside standard Mac/Linux terminal infrastructure using Bash, shell utilities, and text-based tools.
- Proficiency writing advanced queries to extract and prepare data housed in cloud systems such as AWS Athena or PostgreSQL environments.
- Strong communication skills, specifically the assertiveness to defend a technical or mathematical methodology against a project shortcut that threatens calculation validity.
- Upper-intermediate English level.
NICE TO HAVES
- Familiarity with containerized applications managed under Docker or basic Kubernetes infrastructure paradigms.
- Practical knowledge of structural time-series models, Bayesian inference techniques, or custom Markov Chain Monte Carlo (MCMC) configurations.
- Prior experience dealing with multi-account privacy boundaries and strict compliance mandates regarding client data partitioning.
- Background in analyzing digital platform performance APIs, such as Google Ads, Amazon Advertising, or YouTube Data systems.
PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location
职位概览
工作类型
全职
所需技能
Time-series analysisBayesian inference and MCMC methodsHigh-complexity attribution modelingMeridian MMM framework applicationPython backend development with static-typing and structured-schema practicesAdvanced SQL query writingAWS AthenaPostgreSQLMac/Linux terminal and Bash proficiencyData ingestion and ETL from upstream pipelinesModel validation and analytical QA (catching tool/errors and structural flaws)Multi-objective optimization / multi-goal tuningCross-functional collaboration and technical communication (defending methodologies)
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