DeepIntent is hiring a Data Science to join us in developing, implementing, and extending data-intensive machine learning software for real-time auctioning, ad inventory estimation, and audience segmentation.

You will design and implement core components of our algorithms, as well as model and monetize the large amounts of data that Deepintent generates daily. Working with our Data Science and AdServing teams, you will apply Machine Learning to help get things done.

Responsibilities:

• Development and implementation of data-intensive machine learning software for real-time auctioning, ad inventory estimation, audience segmentation, and other adtech applications
• Work with other data scientists, product managers, and software engineers to research and develop Machine Learning driven features
• Proactively analyze raw and statistical data, identify optimization possibilities and come up with possible solutions

Qualifications:

• Experience working with DSPs and bid optimizations
• 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
• Solid Experience with a mix of the following: Java, 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 (xgBoost, TensorFlow, PyTorch, Caffe, Theano, Keras, or alike)

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:

• Experience in Programmatic advertising and RTB
• Deep reinforcement learning (Bellman equations, MDP, policy optimization, credit assignment, multi-agent, …)
• Proficiency with Spark (ML Lib, GraphX), Hadoop, Kafka, Hive
• Record of STEM publications in top journals or conferences
• High rank at Kaggle competitions

Examples of tasks to work on:

• Pay less for traffic where possible (various optimizations
• Inventory analysis
• A lot of BigQuery work, analysis of what inventory we get, at what cost
• Budget strategy (spend more / get more traffic, spend less / get less traffic, other budget pacing strategies

DeepIntent

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