- Predicting true incremental value of merchants, designing and solving an
optimization problem that minimizes the supply / demand gap, optimizing
prices to meet financial and other strategic objectives, developing ensemble
models that collectively support complex decision making processes, etc.
- Flood the room with elegant data science solutions to business problems and
best practices in machine learning.
o Work closely with product and engineering teams to form good hypotheses on
how to improve the business. Design, execute and measure experiments to
test these hypotheses. Productionalize solutions that show lift.
- Interpret and communicate insights and findings from analyses and
experiments to product, service, and business managers
We’re excited about you if you have:
- Advanced degree in Computer Science, Statistics, or similar quantitative field,
with an emphasis on predictive analytics, data mining, statistics, machine
learning, algorithms, etc.
- 4+ years of experience as ML/data scientist or similar role
- Experience with widely used machine learning algorithms like linear and
logistic regression, decision trees, SVM, KNN, k-means, random forest, deep
NN, reinforcement learning, etc.
- Deep understanding of the pros and cons of alternative algorithms in terms of the assumptions made and execution requirements.
- Experience in SQL and NoSQL environments.
- Experience with distributed Big Data technologies (Hadoop, Hive, HBase,
Cassandra, Kafka, Storm, Elastic Search, etc.)
- Experience with open source data pipeline technologies (Oozie, Airflow, Luigi,
- Experience with data visualization, developing Analytics dashboard/deep dive
solutions (Kibana, Tableau, etc.)
- Experience with ML frameworks like TensorFlow, Caffe, Theano, Torch.
- Experience in Python, R and Scala
- Knowledge of ML-as-a-service, Lambda Architecture for ML, developing frameworks for detection of model decay is a big plus