Adobe is looking for a Machine Learning Engineer who will apply AI and machine learning techniques to big-data problems to help Adobe better understand, lead and optimize the experience of its customers.
Partnering with the business units, the candidate will be building various products that address challenging business problems through our customers’ full lifecycle, from customer analytics to marketing media optimization.
By using predictive models, experimental design methods, and optimization techniques, the candidate will be working on the research and development of exciting projects like real-time online media optimization, sales operation analytics, customer churn scoring and management, customer understanding, product recommendation and customer lifetime value prediction.
Ideal candidates will have a strong academic background as well as technical skills including applied statistics, machine learning, data mining, and software development.
Familiarity working with large-scale datasets and big data techniques would be a plus.
What you'll Do
Develop predictive models on large-scale datasets to address various business problems with advanced statistical modeling, machine learning, and analytics techniques.
Develop and implement scalable and efficient modeling algorithms that can work with large-scale data in production systems
Collaborate with product management and engineering groups to develop new products and features.
What you need to succeed
PhD or MS degree in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, Econometrics, or other related fields.
Deep understanding of statistical modeling, machine learning, deep learning, or data mining concepts, and a track record of solving problems with these methods.
Proficient in one or more programming languages such as Python, Java and C
Familiar with one or more machine learning or statistical modeling tools such as R, Matlab and scikit learn
Knowledge and experience of working with relational databases and SQL
Strong analytical and quantitative problem solving ability.
Excellent communication, relationship skills and a strong team player.
Experience with big data techniques (such as Hadoop, MapReduce, Spark)
Knowledge of deep learning, experimental design, ANOVA, statistical inference, and multivariate testing
Knowledge of numerical or combinatorial optimization