Applied Scientist - MEXICO

Key Skills

Aartificial intelligence, AWS, AWS Sagemaker, Cloudera, Data Governance, Databricks, Deep Learning, Google Cloud Platform (GCP), Hortonworks, Keras software library, Kubernetes-K8s, Machine learning techniques, Neural Networks, PowerBI, Python Programming, SAS, SQL, Tableau, TensorFlow

Job Description

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 brought 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
• 5+ years of leveraging data/applied science and modeling methods. Experience in AdTech/MarTech space is a plus.
• At least 2+ years of experience working with digital marketing datasets will be a plus
• Experience with applying statistics and data science tools on large datasets. Extensive experience with data preparation (normalization, scaling, etc.) for modeling
• 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
• 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
• 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.)