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. The Data Scientist’s responsibilities include collaborating with internal and external stakeholders to identify insight requirements and applying creativity to test hypotheses, prepare data, build models, analyze and visualize results, and integrate the solution into innovative data products. The Data Scientist will be a champion of the latest and greatest Machine Learning and Artificial Intelligence technologies, with a specialization in Generative AI.
What you will do:
- 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.
Required Skills:
- 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.
- 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.
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.