Data Scientist Adobe

About the role

The Expert Data Scientist (Adobe Practice) will work hands-on with Machine Learning and Big Data technologies within the Adobe Marketing Cloud (e.g., Adobe Journey Analytics) and third-party tools (e.g., Databricks, R) to build scalable machine learning data products and solutions.

Responsibilities

• Apply state-of-the-art algorithms relying on knowledge of statistical modeling, machine learning, and optimization to develop new data products or improve the performance/quality of existing products.
• Specialize in building data pipelines, developing machine learning models, and performing advanced analytics and statistical analysis.
• Build, evaluate and optimize models which incorporate machine learning, artificial intelligence, and Generative AI.
• Collaborate with internal and external stakeholders to understand business and insight goals, define a learning agenda, and identify relevant KPIs and diagnostics to pursue.
• Collaborate with other data scientists and team leads to define project requirements including data sources, algorithms, and implementation.
• Build expert knowledge of the various data sources brought together for audience segmentation solutions – survey/panel data, 3rd-party data (demographics, psychographics, lifestyle segments), media content activity (TV, Digital, Mobile), and product purchase or transaction data.
• Work with Product and Engineering teams to transition development projects to production systems.
• Prepare and present compelling analytical presentations and effectively communicate complex concepts to marketing and business audiences.
• Provide mentorship and guidance to data scientists where necessary.

Requirements

• Expert-level experience with Adobe’s analytics tools (e.g., Adobe Journey Analytics) and third-party SaaS tools (e.g., Databricks, R).
• Experience with applying statistics and data science tools on large datasets.
• Deep knowledge of supervised vs. unsupervised learning algorithms, including neural networks/deep learning, SVM, decision trees (bagging, random forests, boosting), clustering, regression, and dimensionality reduction techniques.
• Specialization in Generative AI.
• Expert at model training approaches, hyperparameter tuning, tuning learning rates, and model evaluation approaches.
• Extensive experience with data preparation (normalization, scaling, etc.) for modeling.
• Proficient in Python/R, APIs, Excel, LLMs, SQL, and Power Automate.
• Exposure to Spark/PySpark systems in a distributed computing environment.
• SQL mastery, including techniques for writing efficient code over large datasets.
• Ability to leverage critical data-driven thinking and enthusiasm for translating data into actionable insight to generate consistently accurate and useful analysis and models.
• Excel at handling both structured and unstructured data.
• Strong analytical skills and proven track record in deploying innovative SaaS solutions in the tech industry.
• Attention to detail and time management delivering high-quality work for multiple projects across several engagements while meeting deadlines.

Qualifications

• Bachelor’s Degree in a quantitative field (Data Science, Statistics, Math) or related degree programs and 5+ years of relevant work experience OR Master’s Degree in a quantitative field and relevant work experience.
• Proven experience in developing and implementing machine learning models in a business environment.
• Demonstrated ability to handle complex data sets and perform sophisticated data analysis.
• Strong programming skills in Python and/or R.
• Experience with APIs and integrating them into data solutions.
• Advanced proficiency in Excel for data analysis and reporting.
• Familiarity with Large Language Models (LLMs) and their applications.
• Strong SQL skills for database management and data manipulation.
• Strong problem-solving skills and ability to think creatively and critically about data.
• Excellent communication skills, both written and verbal, with the ability to present complex data insights to non-technical stakeholders.
• Ability to work collaboratively in a team environment and mentor junior data scientists.
• Self-motivated with a strong desire to learn and stay updated with the latest advancements in data science and machine learning technologies.