Data Scientist

A successful applicant is expected to be able to collaborate with business partners to develop predictive analytic solutions that enable data-driven strategic decision-making; and utilize data science techniques to manipulate large structured and unstructured data sets, identify patterns in raw data, and develop models to predict the likelihood of a future outcome and/or to optimize business solutions. This level reflects solid knowledge of predictive analytics techniques while continuing to learn how to apply techniques to business issues.

Job Responsibilities

Apply knowledge of sophisticated analytics techniques to manipulate structured and unstructured datasets in order to generate insights to inform business decisions.

Identify and test hypotheses, ensuring statistical significance, as part of building and developing predictive models for business applications.

Translate quantitative analyses and findings into accessible visuals for non-technical audiences, providing a clear view into interpreting the data.

Enable the business to make clear trade-offs between and among choices, with a reasonable view into likely outcomes.

Be responsible for smaller components of projects of moderate-to-high complexity.

Regularly engage with the data science community and participate in cross-functional working groups.

The actual internal level/grade for this role will depend on the candidate's overall experience and skill level Solid knowledge of predictive analytics techniques and statistical diagnostics of models.

Qualifications

• Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.)
• At least 2 - 3 years' of experience in quantitative analytics or data modeling
• Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms
• Knowledge of supervised and unsupervised machine learning methods is required (i.e. regression models, random forests, KNN, NLP, visual recognition, clustering)
• Should possess strong Python skills
• Experience with statistics/machine learning packages such as Spark MLlib, Python (scikit-learn, pandas, numpy, scipy, Matplotlib), Keras, TensorFlow, PyTorch etc.
• should have experience in dealing with Databases (experience with PostGre/ Clickhouse)
• Experience to work with DB’s like PostGre to store Data Science model results
• Capabilities to understand complex code, optimize it and modularize it.
• Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau)