Required Skills 5+ years of experience in data science in and engineering domain 5+ years of production level experience in developing deep learning algorithms and technologies.
5+ years in a computer engineering or IT environment
5+ Strong knowledge of deep learning theory (CNNs, RNNs, LSTMs, etc.).
5+ Strong Python or similar analytics programming experience.
4+ Database technologies and modeling (e.g. SQL, no-SQL, graph databases).
3+ Cloud-based solutions (preferably Azure and or AWS).
At least one major deep learning framework (TensorFlow, Caffe, Theano, Torch, CNTK, MxNet). Hands on experience with statistical modeling, causal identification and machine learning software (Python statistical and ML libraries, R, etc.).
Containerized solutions (Docker Kubernetes)
Experience and Education BS in Mathematics, Statistics, Computer Science, Economics, Physics, or other behavioral and or equivalent quantitative science Interested in contributing your opinions and perspective to solve the challenges that our users face Possess extensive end-to-end project experience using machine learning, deep learning, optimization, or causal inference to solve complex real-world problems.
Have a proven track record of delivering impactful results and high-quality work on tight schedules Have experience using PythonR for data analysis, modeling, and writing production code Experience with defining key product metrics, setting team goals, and building internal tools to monitor progress against KPI's Strong problem-solving skills and ability to learn quickly. Being up to date on trends and developments in deep learning. Proven ability to work both independently and in team-based environment.
Commitment to producing high quality, well-designed and flexible solutions. Solid troubleshooting, analytical, and organizational skills with attention to detail. Outstanding written and verbal data storytelling, demonstrated consulting skill and ability to tailor communication style and depth to a variety of audiences Experience in Python, Pandas, NumPy, Data Visualization, Data Cleaning, Command Line, Git and Version Control, SQL, API's, Probability Statistics, Data Manipulations Experience in Implementing Cross Validations to validate the stability, or accuracy, of machine-learning model Experience in Implementing Clustering which receives inputted data and finds similarities in the data Experience in Implementing Deep Learning, Linear Regression, AB Testing Experience in Implementing Hypothesis Testing, Statistical Power, Standard Error, Causal Inference. Experience on Azure Data Bricks Experience on T-Sql, Python, R, Spark.
