Sr. Data Scientist
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A Sr. Data Scientist at Zappos is responsible for driving the customer experience forward leveraging a combination of existing machine-learning tools coupled with custom techniques catered toward the problems at hand. You will be part of a small team dealing with high stakes problems without obvious or straightforward solutions. You will have relative autonomy when it comes to the methodologies and approaches you take to solving these business and customer facing problems. We are looking for a candidate with a natural curiosity, cutting edge skills and a hunger to stay abreast of the latest in the field.
We are looking for a generalist Sr. Data Scientist with deep knowledge in at least one of the following fields- NLP, Computer Vision, or Forecasting to support the on-going initiatives on the team.
What you'll be doing:
- Building meaningful and memorable experiences for our customers through machine learning driven systems
- Finding innovative solutions to loosely structured novel problems
- Designing and implementing scalable and reliable approaches to extract the best information possible from the data.
- Gathering and transforming complex datasets to build features feeding into your machine learning models
- Going beyond your existing toolkit to deliver a solution catering to multiple stakeholders
- Sharing your knowledge base with others and mentoring more junior team members
- Utilize your excellent interpersonal, written and verbal communication skills to explain your finding or solutions
- Make key decisions and take ownership of your own projects and be comfortable playing the role of an autonomous contributor when needed.
What you’ll bring to the table:
- At least 3+ years of experience as a Data Scientist in industry setting
- Education - M.S. in Computer Science, Mathematics, Economics, Operations Research or related field.
- Programming skills in Python or Java. Experience with statistical programming languages.
- Strong mathematical knowledge and understanding of machine learning.
- Strong understanding of the ML process including training and validation, regularization, variance-bias tradeoff, dimensionality reduction, hyper-parameter optimization.
- You are highly skilled across multiple data science disciplines with experience in a variety of Data Science algorithms including but not limited to, tree-based models, SVMs, collaborative filtering, genetic algorithms, clustering and forecasting.
- Experience with statistical modeling techniques such as generalized linear models, forecasting, probability theory, experimental design and strong mathematical understanding of fundamentals such as variance, key distributions.
- Familiarity with deep learning techniques such as RNNs, CNNs and LSTM Networks, along with an understanding of the implications of various tuning parameters.
- Excellent interpersonal, written and verbal communication skills.
- Knowledge of cloud-based solution such as Redshift, S3, EC2, Kinesis, Amazon EMR, DynamoDB.
Something you didn't know about the team:
Everyone on the team has at least one “a” in either their first or last name. Not saying this is required, but a definite pattern has been established.
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