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GDNA - Senior Data Scientist
Karnataka, India
Actively hiring, first seen by us today- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 10, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
abgcareers is hiring
26 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
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- XGBoost
Where the posting names them
Advanced Data Science & AI Implementation - Develop and apply cutting-edge machine learning techniques such as ANN, XGBoost, and deep learning models to solve complex business problems. - Ensure the use of state-of-the-art AI frameworks and methodologies to enhance model accuracy, efficiency, and scalability.
Your next step
Complete your application on abgcareers.peoplestrong.com. The employer’s form will show what is required.
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Demand for Data Scientist roles
New Data Scientist postings at the employers we follow are down 5% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Job description
Job Purpose
The role focuses on implementing data science solutions and best practices while managing their lifecycle within a business unit. It involves translating business needs into technical solutions, ensuring data quality, and optimizing analytical workflows to drive innovation and efficiency.
Business Dimensions (as applicable) (Add Rows as needed)
Number of group businesses & functions to work with including new age businesses
- 1
Number of AI Models Deployed & Continuously Improved
- 2+
Number of distinct KPIs impacted
- 1+
Indirect influence across ABG (beyond GDNA)
- 1-2 ABG business using the AI solution and algorithm noted in the point above
Hands-on and deep experience of DE/DS languages and tools
- 3-5
Job Context
This role is responsible for driving the execution of AI and Data initiatives within a business unit, ensuring seamless integration with strategic objectives. It involves working closely with stakeholders to design, deploy, and operationalize data-driven innovative solutions while ensuring measurable business impact. The role emphasizes structured project execution, continuous improvement, and model scalability, ensuring AI solutions are effectively adopted by business teams. Additionally, the role plays a key part in mentoring junior team members, fostering AI & Data skill development, and overseeing the successful Build-Operate-Transfer (BOT) of AI solutions for long-term sustainability.
The difficulty this role faces is to learn multiple technology fast.
Principal Accountabilities (Add Rows as needed)
Advanced Data Science & AI Implementation
- Develop and apply cutting-edge machine learning techniques such as ANN, XGBoost, and deep learning models to solve complex business problems.
- Ensure the use of state-of-the-art AI frameworks and methodologies to enhance model accuracy, efficiency, and scalability.
- Optimize AI models by fine-tuning hyperparameters, feature engineering, and advanced algorithm selection to maximize performance.
End-to-End Solution Development
- Oversee the full AI project lifecycle, from data preparation and model development to deployment and monitoring.
- Ensure smooth integration of AI solutions into production environments, collaborating with engineering teams for scalable deployments.
- Use project management tools like JIRA to track tasks, deadlines, and progress, ensuring structured execution of AI initiatives.
- Maintain a structured framework for model versioning, documentation, and reproducibility to ensure long-term sustainability.
Experimentation & Continuous Improvement
- Implement a systematic experimentation approach to identify gaps in current AI models and refine them iteratively.
- Conduct A/B testing, error analysis, and post-deployment monitoring to assess model effectiveness and business impact.
- Drive an innovation-first mindset, experimenting with new algorithms and methodologies to improve existing solutions.
- Establish a feedback loop between model performance and business outcomes, ensuring AI-driven insights remain relevant and impactful.
Technical Leadership & Best Practices
- Enforce best practices in coding, model documentation, and version control to enhance standardization across AI initiatives.
- Ensure teams stay updated with emerging AI/ML methodologies and provide structured guidance for integrating new techniques.
- Act as a mentor to junior data scientists, providing hands-on guidance while establishing technical excellence within the team.
Cross-Team Collaboration & Stakeholder Management
- Ensure seamless integration of AI solutions into business workflows.
- Lead discussions with stakeholders to align AI initiatives with operational priorities and measurable outcomes.
- Standardize communication channels to ensure clarity, accountability, and alignment across teams.
Scalable AI Solutions & Reusability
- Drive the standardization of AI solutions to enable their scalability and adaptability across multiple business functions.
- Develop reusable AI frameworks, ensuring consistency and reducing redundancy in data science efforts.
- Establish best practices for documenting and sharing AI learnings across teams, improving operational efficiency and knowledge transfer.
Business KRA (as applicable) (Add Rows as needed)
Non-Monetary
- Data & AI driven improvement in KPIs relevant to a particular business or initiative such as quality, CX, stability, risk, efficiency
Decision making & augmentation
- Decisions replaced or augmented with the use of data & AI
- Decisions improved through the use of data & AI
AI Capability
- Drive, through examples, the data driven decision making culture
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Karnataka, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.