Local Team Lead

hace 1 mes


Mexico City Acxiom A tiempo completo
As part of the Analytics Services, the Applied Scientist will lead the charge in developing machine learning and statistical models to support and expand the Audience Propensities product suite for our domestic and global businesses. The Applied Scientist’s responsibilities will include 1) partnering with product, engineering and business stakeholders to define digital product scope and requirements, 2) building models, analyzing/visualizing the results and integrating the solution into our suite of data products, 3) developing new analytical reports to help us better understand our data. The Applied Scientist will be a champion of the latest Machine Learning and Artificial Intelligence technologies and will not only be an advocate but will also lead by example in influencing adoption.

Location: Lomas Atlas, Mexico City, MX

Duties and Responsibilities

Build expert knowledge of the various data sources together for audience propensities solutions – survey/panel data, 3rd-party data (demographics, psychographics, lifestyle segments), media content activity (TV, Digital, Mobile, Automotive), and product purchase or transaction data

Apply state-of-the-art algorithms relying on knowledge of statistical modeling, machine learning, and optimization to develop new audience propensities & analytical data products or improve the performance/quality of existing audience propensities and data products

Build, evaluate and optimize models which incorporate machine learning and artificial intelligence

Be a thought leader and champion for adoption of new technologies and enable migration to new cloud based ML stack

Collaborate with internal and external stakeholders to understand business goals and product economics, and identify relevant KPIs to assess product in-market performance

Work with other Applied and Data scientists and team leads to define project requirements including data sources, algorithms, and implementation

Partner with Product and Engineering teams to transition development projects to production systems

Effectively communicate complex data science/ML concepts to marketing and business audiences

Qualifications

Experience Required

Proficient English writing and speaking

Very strong data science / machine learning experience.

5+ years of leveraging data/applied science and modeling methods.

Experience with Pyspark, AWS Infrastructure and MLOps

Experience with applying statistics and data science tools on large datasets.

Extensive experience with data preparation (normalization, scaling, etc.) for modeling

5+ years of experience in building, testing and deploying production ready models in python, R, spark, Julia or similar languages and experience using Scikit learn, MLlib or similar packages

At least 3+ years of experience building ETL transformation, SQL, modeling & mlops pipelines

Working knowledge of supervised vs. unsupervised learning algorithms, including linear/logistic regression, neural networks/deep learning techniques, SVM, decision trees (bagging, random forests, boosting), XG Boost, clustering, regression, and dimensionality reduction techniques

Strong skills on model training approaches, hyperparameter tuning, and model evaluation approaches

Ability to leverage critical data-driven thinking and translating data into actionable insight to generate consistently accurate and useful analysis and models

Attention to detail and time management delivering high quality work for multiple projects across several engagements while meeting deadlines

Bachelor’s Degree in a quantitative field (Data Science, Statistics, Math) or a related degree program and 5+ years of relevant work experience OR Master’s Degree in a quantitative field and 3+ years relevant work experience

What will set you apart

Experience with E2E ML platform such as H20, Databricks, AWS Sagemaker, Google AI/ML platform

Experience with using tools such as Airflow, KubeFlow for model deployment will be a plus

CI/CD/MLOps experience - container-based model deployment frameworks using (Kubernetes, Docker, ECS/EKS or similar) will be a plus

At least 2+ years experience in leveraging Deep Learning, Neural Network based modeling frameworks (Tensorflow, Keras)

At least 2+ years of experience deploying data/analytical products at scale using Cloud technologies e.g. AWS Sagemaker, Google Cloud Platform, etc.)

At least 2+ years of experience working with digital marketing datasets will be a plus

Experience in AdTech/MarTech space is a plus.

Primary Locations:

Mexico City

Additional Locations (if applicable):


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