Data Scientist / Machine Learning Engineer
- 305 Main St, Redwood City, CA 94063, USA
PubMatic is a publisher-focused sell-side platform for an open digital media future.
Featuring leading omni-channel revenue automation technology for publishers and enterprise-grade programmatic tools for media buyers, PubMatic's publisher-first approach enables advertisers to access premium inventory at scale.
Processing over one trillion ad impressions per month, PubMatic has created a global infrastructure to drive publisher monetization and control over their ad inventory.
Since 2006, PubMatic's focus on data and technology innovation has fueled the rise of the programmatic industry as a whole. Headquartered in Redwood City, California, PubMatic operates 13 offices and six data centers worldwide.
We are looking for a strong Data Scientist or Machine Learning Engineer (MLE) - a proven 'doer' to develop, implement and extend data-intensive ML software for real-time auctioning, ad inventory estimation, and audience segmentations.
You will design and implement core components of our algorithms, as well as model and monetize the terabytes of structured data that PubMatic generates daily.
Working with our Data Science and Ad Serving teams, you will apply ML to help get things done.
Development and implementation of data-intensive ML algorithms and software for real-time auctioning, ad inventory estimation, audience segmentations, and related AdTech applications.
Working with data scientists, product managers, and software engineers to develop and support the software for new ML products.
Ensuring excellence in delivery to internal and external customers
MS / PhD in STEM field
3+ years of hands-on industry work experience designing and building large-scale ML algorithms and ETL that are well-designed, cleanly coded, well-documented, operationally stable, and timely delivered
5+ years total analytical work, including academic research
Solid experience with:
Python or R, including ML libraries (SKLearn, NumPy, caret, e1071, …), including CPU/GPU parallelization, matrix algebra, vectorization, linear programming, lambda programming, OOP, …
At least one of the DL frameworks (TensorFlow, PyTorch, Caffe, Theano, Keras, or alike)
Solid understanding of:
Graduate statistics and probability (inference, hypothesis testing, p-value, ANOVA, CLT, LLN, Bayes’ theorem, A/B testing, combinatorics, PDF/CDF, joint/conditional/marginal densities)
Vector calculus (gradients, Jacobians, partial derivatives and integrals, optimization)
Linear algebra (eigen values/vectors, inverses, decompositions, orthogonality, multi-linear)
Time series (ARIMA, GARCH, forecasting, Kalman filter)
Shallow ML algorithms: regressions, SVM, kMeans, kNN, NB, HMM, PCA, NMF, SVD, XGBoost, decision trees, ensemble methods (random forest)
Deep NN algorithms: MLP, RNN, LSTM, CNN, GRU
ML concepts: backprop, hyperparameter tuning (Bayesian optimization, grid/random search), regularization, learning rate, optimization
Advanced work with SQL or NoSQL, including nested/join/aggregate queries, stored procedures, over partition by, basic stat functions
Cloud compute engines (AWS, Azure, GCP and alike), ML on clusters of GPUs, SageMaker, Jupyter
Excellent communication skills, cultural fit and natural curiosity in learning the ML developments and domain expertise
Nice to have:
Prior experience with programmatic advertising and RTB
Deep reinforcement learning (Bellman equations, MDP, policy optimization, credit assignment, multi-agent)
Proficiency with Spark (ML Lib, GraphX), Hadoop, Kafka, and/or Hive
Proficiency with Scala, Java, and/or C/C++
Record of STEM publications in top journals or conferences
High ranking at Kaggle competitions
What's the first step?
Please complete this quick self-ranking of your strengths, and we can get you started!
PubMatic is proud to be an equal opportunity employer; we don’t just value diversity, we promote and celebrate it.
We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
All your information will be kept confidential according to EEO guidelines.