Senior Data Scientist II

Elsevier

5 - 10 years

14 - 19 Lacs

bengaluru

Posted:1 day ago| Platform:

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Skills Required

computer science data analysis scientist iii legal professional data science machine learning big data analytics python productivity improvement

Work Mode

Work from Office

Job Type

Full Time

Job Description

Responsibilities:

Working closely with other data scientists and engineers to design, develop, and deploy AI solutions.
Leading the development of advanced AI and machine learning models to solve complex business problems.
Analyzing large datasets to identify patterns and trends that can inform AI development.
Collaborating with cross-functional teams to ensure AI solutions are aligned with business goals and customer needs.
Evaluating and creating new frameworks and defining methodologies and governance.
Mentoring junior data scientists and provide guidance on AI and machine learning best practices.
Working with product leaders to apply data science solutions. Leading small- to medium-sized teams (direct or indirect).

Requirements:

Possess an advanced or Masters education in Computer Science, Mathematics, Statistics, or a related field.
5+ years of experience using NLP tools and methods such as OpenNLP, Stanford NLP, LDA, Gensim, spaCy
Be proficient in working with big data technologies and tools like Hadoop, Spark, or AWS.
Possess advanced programming skills in Python, R, or other relevant languages for data analysis.
Have experience working directly with large language models and transformer-based architectures including BERT, RoBERTa, T5 etc.
Be proficient in working with big data technologies and tools.
Experience using Machine Learning and associated packages like scikit-learn, pandas, NumPy.
Have experience and knowledge of applying LLMs including ChatGPT, GPT 3.5, Claude, Mistral, etc.
Be a natural problem solver, able to lead and mentor junior data scientists, and collaborate with cross-functional teams.
Demonstrate proficient experience with machine learning algorithms, including deep learning, gradient boosting, and random forests.

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