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IBM
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Data Scientist

Data Scientist

IBM

3 - 6 years

7 - 12 Lacs

bengaluru

Posted:11 hours ago| Platform:

Apply

Skills Required

python natural language processing machine learning sql tensorflow transformers data analytics ibm cloud predictive analytics microsoft azure jupyter notebook numpy artificial intelligence docker pandas git data science pytorch api aws flask ml

Work Mode

Work from Office

Job Type

Full Time

Job Description

Role Overview:As an AI Engineer/SW Developer, you will be in a unique position to combine your strategic thinking with your technical skills in AI, machine learning, and data analytics. You will apply your skills to help implement data-driven solutions that align with business goals. You will steer enterprise projects that improve decision-making, solve complex problems, and drive business growth. This role involves working with team members and stakeholders to translate data insights into actionable recommendations that deliver meaningful business impact.

Key Responsibilities:1. Implement AI, Data Science, and Technical Execution:

Support the design, implementation and optimization of AI-driven strategies per business stakeholder requirements.

Design and implement machine learning solutions and statistical models, from problem formulation through deployment, to analyze complex datasets and generate actionable insights.

Apply GenAI, traditional AI, ML, NLP, computer vision, or predictive analytics where applicable.

Collect, clean, and preprocess structured and unstructured datasets.

Help refine data-driven methodologies for transformation projects.

Learn and utilize cloud platforms to ensure the scalability of AI solutions.

Leverage reusable assets and apply IBM standards for data science and development.

Apply ML Ops and AI ethics.

2. Strategic Planning & Execution

Translate business requirements into technical strategies.

Ensure alignment to stakeholders’ strategic direction and tactical needs.

Apply business acumen to analyze business problems and develop solutions.

Collaborate with stakeholders and team to prioritize work.

3.Project Management and Delivering Business Outcomes:

Manage and contribute to various stages of AI and data science projects, from data exploration to model development to solution implementation and deployment.

Use agile strategies to manage and execute work.

Monitor project timelines and help resolve technical challenges.

Design and implement measurement frameworks to benchmark AI solutions, quantifying business impact through KPIs.

4.Communication and Collaboration:

Communicate regularly and present findings to collaborators and stakeholders, including technical and non-technical audiences.

Create compelling data visualizations and dashboards.

Work with data engineers, software developers, and other team members to integrate AI solutions into existing systems.

Required education Bachelor's Degree Required technical and professional expertise Experience:
Hands-on Experience with AI/ML technologies and statistical modelling through coursework, projects, or past internships or full-time positions. Participation in AI/Data-related summits will be an added advantage ( eg. Kaggle/Hackathons)

Experience with prompt engineering or fine-tuning LLMs.

Familiarity with tools like Lang Chain, Hugging Face Transformers, or OpenAI APIs.

Understanding of model evaluation metrics specific to LLMs

Technical
Skills:

Proficiency in SQL and Python for performing data analysis and developing machine learning models.

Experience and/or coursework in statistics, machine learning, generative and traditional AI.

Knowledge of common machine learning algorithms and frameworkslinear regression, decision trees, random forests, gradient boosting (e.g., XGBoost, LightGBM), neural networks, and deep learning frameworks such as TensorFlow and PyTorch.

Familiarity with cloud-based platforms and data processing frameworks.

Understanding of large language models (LLMs).

Familiarity with object-oriented programming.

Experience and/or coursework with common Python libraries used by data scientists (e.g., NumPy, Pandas, SciPy, scikit-learn, matplotlib, Seaborn, etc.)

Knowledge of APIs, Docker, Flask, or model serving technologies

Experience with tools like Jupyter, Git, or cloud platforms (AWS, Azure, IBM Cloud)

Strategic and Analytical
Skills:

Strategic thinking and business acumen.

Strong problem-solving abilities and eagerness to learn.

Ability to work with datasets and derive insights.

Attention to detail.

Communications and Soft
Skills:

Excellent communication skills, with the ability to explain technical concepts clearly.

Independent and team oriented.

Understands AI Ethics principles.

Works openly and inclusively.

Adaptable to fast-paced environments.

Enthusiasm for learning and applying new technologies.

Growth mindset.

Ability to balance multiple initiatives, prioritize tasks effectively, and meet deadlines in a fast-paced environment.

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