Capgemini is seeking ambitious and motivated fresh graduates for the Data Scientist Trainee – On-the-Job Training Program. This program is designed to provide hands-on experience in data analytics, machine learning, and AI-driven solutions while working on real-world projects. Trainees will learn to analyze complex datasets, develop predictive models, and support data-driven decision-making across various business domains. The program aims to equip participants with practical skills, industry exposure, and mentorship from experienced professionals, preparing them for a successful career in data science.
Key Responsibilities:
-
Assist in gathering, cleaning, and processing structured and unstructured datasets.
-
Develop, test, and deploy machine learning models under guidance.
-
Conduct exploratory data analysis to identify patterns, correlations, and insights.
-
Create reports, dashboards, and visualizations to communicate findings to stakeholders.
-
Collaborate with cross-functional teams including data engineers, business analysts, and domain experts.
-
Document methodologies, processes, and outcomes for internal knowledge sharing.
-
Stay up-to-date with the latest tools, technologies, and trends in data science.
-
Participate actively in training sessions, workshops, and project discussions.
Required Skills and Qualifications:
-
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Information Technology, or related fields.
-
Basic programming knowledge in Python, R, or SQL.
-
Familiarity with data analysis libraries (Pandas, NumPy) and visualization tools (Matplotlib, Seaborn, Tableau, Power BI).
-
Understanding of fundamental AI/ML concepts and algorithms.
-
Strong analytical, problem-solving, and logical reasoning skills.
-
Good verbal and written communication skills in English.
-
Curiosity, eagerness to learn, and a proactive approach to tasks.
-
Ability to work collaboratively in a team-oriented environment.
Experience:
-
Fresh graduates (2023, 2024, or 2025 batch) are eligible.
-
Academic projects, internships, or certifications in data science, machine learning, or AI are advantageous but not mandatory.
Working Hours:
-
Full-time training program with standard working hours (typically 9:00 AM – 6:00 PM, Monday to Friday).
-
Some flexibility may be required based on project demands and learning modules.
-
Training and work location may include on-site, remote, or hybrid models, depending on Capgemini policies.
Knowledge, Skills, and Abilities:
-
Ability to handle and analyze large datasets efficiently.
-
Understanding of statistical analysis, predictive modeling, and AI/ML workflows.
-
Critical thinking and problem-solving aptitude.
-
Effective communication and presentation skills to convey technical insights to non-technical stakeholders.
-
Attention to detail and strong documentation practices.
Benefits:
-
Practical exposure to real-time data science projects and business problems.
-
Mentorship and guidance from experienced data scientists and industry experts.
-
Access to Capgemini’s learning platforms, tools, and resources.
-
Internship/training completion certificate to enhance career prospects.
-
Networking opportunities within Capgemini’s professional ecosystem.
-
Potential transition to full-time employment upon successful program completion.
Why Join Capgemini:
Capgemini is a global leader in consulting, technology services, and digital transformation. Joining the Data Scientist Trainee Program offers a unique opportunity to work on cutting-edge projects, gain hands-on experience, and develop industry-relevant skills. Capgemini fosters a collaborative, innovative, and growth-oriented environment, empowering fresh graduates to build a strong foundation for a rewarding career in data science and analytics.
How to Apply:
Candidates can apply via Capgemini’s official careers portal by submitting an updated resume, academic transcripts, and any relevant project work or certifications. Shortlisted candidates will undergo an online assessment, followed by technical and HR interviews. Ensure your contact information is accurate to receive updates regarding assessment schedules, interview rounds, and onboarding procedures.
