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Data Scientist - II
G2 Crowd
·
4-6 years
₹ 17 - 17L/yr (AmbitionBox estimate)
Bangalore / Bengaluru
1 vacancy
Data Scientist - II
G2.com
posted 2mon ago
Job Role Insights
Flexible timing
Key skills for the job
Job Description
In this role, you will:-
Modeling and Statistical Analysis (50%):
- Independently lead the development of machine learning models, owning feature engineering, extraction, model selection, and optimization
- Design experiments by formulating statistical hypotheses, defining data requirements, pre-processing and cleaning the data, and performing the hypothesis testing.
- Operationalise models at scale applying AI and engineering best practices by working with the ML engineers.
- Experiment with various algorithms and techniques to advance model performance.
- Define feedback and evaluation methods for the business problems.
- Demonstrate excellent coding and debugging skills.
- Make impactful contributions by leveraging AI and Machine Learning expertise to address pressing business challenges.
- Collaborate with cross functional teams to understand the business requirements and data architecture.
- Translate business requirements into technical solutions by working with the business and senior data scientists.
- Identify and document the data requirements and manage data collection and preparation for projects.
- Design and document training and testing strategy.
- Document methodologies, findings, and outcomes of model experiments and present it to the team and key stakeholders.
- Mentor junior team members, providing technical support, guidance on model development, and best practices implementation.
- Coach junior team members, helping them understand complex datasets, models, and business requirements by giving clear and actionable feedback.
- Encourage the development of best practices and innovative approaches in data analysis and modeling.
- 4-6 years experience as a data scientist involved in data extraction, analysis and modeling.
- 4+ years of experience in Python and SQL or related tools for machine learning.
- Strong understanding of statistics and linear algebra.
- Proficiency in machine learning algorithms and all stages of machine learning.
- Familiarity with neural networks and deep learning.
- Familiarity with AWS services and Snowflake.
- Proficiency in handling structured and unstructured data.
- Successful end-to-end delivery of data science products.
- Exposure to MLOps tools like MLFlow, KubeFlow, DVC,AWS Sagemaker, Seldon etc
- Experience deploying models in a AWS cloud environment - with specific experience with AWS tools such as Sagemaker and Step Functions.
- Expertise with Natural Language Processing and Understanding.
- Experience with libraries and frameworks for training ML and DL models (PySpark, Tensorflow).
- Experience and expertise in ML Operations best practices.
Employment Type: Full Time, Permanent
Functional Areas: Analytics & Business Intelligence
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