Data Analyst - Machine/Deep Learning

• Company Huquo Consulting Pvt. Ltd
• No. of Openings 10
• Salary Not Disclosed
• Work Type on-site

Job Description :

- Be involved in the approach to the problem, structure and delivery of the solution.
- Contribute to how analytical approach is structured for specification of analysis.
- Solve business problems by applying advanced Machine Learning algorithms and complex statistical models on large volumes of data.
- Contribute insights from conclusions of analysis that integrate with initial hypothesis and business objective. Independently address complex problems.
- Extremely comfortable working with data, including managing large number of data sources, analyzing data quality, and pro-actively working with client's data.
- Become an expert in clients information assets.
- Build, scale and deploy holistic data science products after successful prototyping.
- Reformulate highly technical information into concise, understandable terms for presentations.
- Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team.

Qualifications :
- 2-7 years experience with solid analytical skills and an entrepreneurial, hands-on approach.
- Strong hands-on programming skills in Python, SQL. Experience with large data sets and analytical tools.
- Excellent understanding of traditional machine learning techniques and algorithms, such as K-means, KNN, SVM, Random Forests, etc. Experience using ML, DL frameworks (such as Scikit-learn, XGBoost, TensorFlow, Keras, etc.)
- Strong experience in presenting analytical insights using powerpoint/excel. Case interview is expected.
- Candidates who have published white papers, research papers in the field of computer science and data science are desirable.
- Prior experience in management consulting and/or analytics based consulting is desirable.
- Preferred domain expertise in Retail, Media or Sports.
- Very strong analytical skills with the demonstrated ability to research and make decisions based on the day-to-day and complex customer problems required.