Requirements: English
Company: Tata Consultancy Services
Region: Amsterdam , North Holland
About TCS
Tata Consultancy Services (TCS) is a global leader in IT services, digital, and business solutions that partners with its clients to simplify, strengthen, and transform their businesses. We ensure the highest levels of certainty and satisfaction through a deep-set commitment to our clients, comprehensive industry expertise, and a global network of innovation and delivery centers.
TCS operates on a global scale & has over 6,08,985 of the worlds best-trained consultants representing 152 nationalities in 53 countries. TCS established its presence in the Netherlands in 1992 and works with leading Dutch Customers across industry sectors, including several NL20 companies. TCS has been ranked #1 in customer satisfaction, among IT services providers in the Netherlands in a survey by Whitelane Research and is recognized as a Top Employer in the Netherlands, Europe & also Global by the Top Employers Institute
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Role description
TCS is looking for a highly skilled and motivated Data Scientist to join our team. In this role, you will be responsible for analyzing large sets of complex data to help guide strategic decision-making, uncover trends, and solve business problems. The ideal candidate will have a strong background in data analysis, statistical modeling, machine learning, and programming, along with the ability to communicate findings clearly to both technical and non-technical stakeholders.
Responsibilities:
Data Collection & Preparation:
- Gather, clean, and preprocess large datasets from various sources (databases, external datasets, APIs).
- Perform exploratory data analysis (EDA) to understand the structure and quality of the data.
- Apply data wrangling techniques to handle missing, inconsistent, or incomplete data.
Statistical Analysis & Modeling:
- Use statistical techniques to identify patterns, correlations, and trends in data.
- Develop predictive and prescriptive models using machine learning algorithms (supervised and unsupervised learning).
- Build, test, and optimize models (e.g., regression, decision trees, random forests, SVM, deep learning, etc.).
- Perform hypothesis testing and A/B testing to validate assumptions and recommendations.
Machine Learning & AI Implementation:
- Implement machine learning models and algorithms for various business applications such as classification, clustering, recommendation systems, and forecasting.
- Leverage deep learning techniques and neural networks when necessary for complex problems.
- Monitor the performance of deployed models, providing regular updates and improvements as needed.
Data Visualization & Reporting:
- Create interactive and insightful visualizations using tools such as Tableau, Power BI, or libraries like Matplotlib and Seaborn (for Python).
- Present complex technical findings to non-technical stakeholders using clear, actionable insights.
- Prepare detailed reports and dashboards that track key performance indicators (KPIs) and other business metrics.
Collaboration & Communication:
- Work closely with cross-functional teams, including business analysts, product managers, and engineers, to define project goals and requirements.
- Communicate findings, methodologies, and insights effectively to both technical and business audiences.
- Provide actionable recommendations to help drive data-informed decision-making.
Continuous Improvement:
- Stay up-to-date with the latest research, tools, and techniques in data science and machine learning.
- Experiment with and implement cutting-edge machine learning algorithms and techniques.
- Contributes to the refinement and optimization of existing data models and processes.
Data Governance & Ethics:
- Ensure data integrity and privacy by following best practices in data handling and processing.
- Work in compliance with data security standards and ethical guidelines.
Skills and Qualifications:
Educational Background:
- Bachelors or Masters degree in Data Science, Computer Science, Mathematics, Statistics, or a related field. PhD is a plus.
Technical Skills:
- Strong proficiency in programming languages such as Python, R, or Java.
- Solid knowledge of statistical analysis and machine learning techniques.
- Hands-on experience with data manipulation and analysis using libraries like Pandas, NumPy, Scikit-learn, etc.
- Familiarity with big data technologies such as Hadoop, Spark, or similar.
- Experience with databases (SQL, NoSQL) and data extraction techniques.
- Familiarity with cloud platforms such as AWS, GCP, or Azure is a plus.
Analytical Skills: