Talent Acquisition at Tredence Analytics
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Tredence - Data Scientist (1-7 yrs)
As a Data scientist you will solve some of the most impactful business problems for our clients using a variety of AI and ML technologies. You will collaborate with business partners and domain experts to design and develop innovative solutions on the data to achieve predefined outcomes.
Roles & Responsibilities :
- Engage with clients to understand current and future business goals and translate business problems into analytical frameworks
- Develop custom models based on in-depth understanding of underlying data, data structures, and business problems to ensure deliverables meet client needs
- Create repeatable, interpretable and scalable models
- Effectively communicate the analytics approach and insights to a larger business audience
- Collaborate with team members, peers and leadership at Tredence and client companies
- Bachelor's or Master's degree in a quantitative field (CS, machine learning, mathematics, statistics) or equivalent experience.
- 2-7 years of experience in data science, building hands-on ML models
- Experience leading the end-to-end design, development, and deployment of predictive modeling solutions.
- Excellent programming skills in Python. Strong working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, Jupyter, etc.
- Advanced SQL skills with SQL Server and Spark experience.
- Knowledge of predictive/prescriptive analytics including Machine Learning algorithms (Supervised and Unsupervised) and deep learning algorithms and Artificial Neural Networks
- Experience with Natural Language Processing (NLTK) and text analytics for information extraction, parsing and topic modeling.
- Excellent verbal and written communication. Strong troubleshooting and problem-solving skills. Thrive in a fast-paced, innovative environment
- Experience with data visualization tools - PowerBI, Tableau, R Shiny, etc. preferred
- Experience with cloud platforms such as Azure, AWS is preferred but not required.