Senior Data Scientist(AI/ML)

The Data Scientist employs big data technologies and analytics techniques to process and analyze

large amounts of data to derive insights and predictions that help improve business decisions. In

order to do this, the Data Scientist must be able to organize data, cleanse and normalize raw data,

process data for statistical modeling using computers, and identify relationships and trends in data.

The Data Scientist must demonstrate strong programming skills and be able to develop, test, and

deploy solutions that address different business problems. The Data Scientist must also have a

strong educational foundation in data analytics, statistical modeling, advanced machine learning,

artificial intelligence, database technology, computer programming, and software engineering.

The Data Scientist must be able to think analytically, and must be detail oriented. Also, the Data

Scientist should be able to effectively communicate with supervisors and business leaders who

may not have strong data analytics or statistical background.

The Data Scientist will usually work independently with guidance provided by the senior team

members.

Responsibilities

Responsible for data quality and data inspection

Create insightful dashboards and data reports

Produce query for data preprocessing, feature engineering, and data enrichment

Design, create, test and implement complex predictive ML algorithms

Implement advanced analytics, machine learning, and AI techniques to derive business

• value extracted from internal and external data sets, leveraging cloud-based technology

Create robust code to train and deploy predictive models

Responsible for developing analytical solutions to solve real business problems

Give insights to product-teams through presentation of data-driven recommendations

Possess strong problem-solving skills with an emphasis on product development

Qualifications

5+ years of experience as a data scientist;

Good understanding of applying data science and machine learning methods to create value out of data

Hands-on experience with machine learning, artificial intelligence, applied statistics, and/or

operations research techniques and approaches (regression, classification, simulation

modeling, hypothesis testing, etc)

A good understanding of the data science process

Able to work well in a team and with agile development processes

Ability to distill actionable insights from analysis for a non-technical audience

A good working level knowledge of languages such as Python, R, matlab, Julia, Scala;

knowledge of pandas, scikit-learn, numpy, tensorflow, and other scientific libraries/modules

for data science

• Familiar with SQL and has experience querying databases
• Experience with large datasets and the ability to optimize queries