HCLTech is a global technology company, home to more than 224,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud, AI and software, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending December 2023 totaled $13.1 billion.

To learn how we can supercharge progress for you, visit hcltech.com.

Data Scientist

Data scientists are specialists who turn raw data into valuable insights and actionable decision. They use their analytical skills, programming skills, statistical expertise, and knowledge of machine learning to build advance analytics data product that help the company make better decisions, improve products, and understand customers.

In this role we value:

Analytical Skill

Data scientists must be adept at analyzing complex data sets, using statistical methods and/or algorithms to uncover patterns, trends, and insights that can inform or assist decision-making by following scientific approach. This includes:

• Data cleaning and preprocessing: Transforming raw data into a usable format.
• Statistical analysis: Applying statistical methods to identify patterns and trends.
• Machine learning/ Optimization: Utilizing algorithms to build predictive and optimization models.
• Data visualization: Creating meaningful charts and graphs to communicate findings.
• Experimentation Design: Designing how to test with scientific measurement to confirm the results from the model

Communication

Effectively communicating findings to stakeholders is crucial. Data scientists need to translate complex data analyses into understandable insights and recommendations, often using data visualization tools. This involves:

• Presenting findings: Clearly explaining results to both technical and non-technical audiences.
• Creating reports and presentations: Documenting analyses and presenting insights with good storytelling.
• Collaborating with stakeholders: Working effectively with teams to translate data into action also educate&help stakeholders on the understanding of key assumption and possibility of the advance analytics techniques.

Curiosity and Problem-Solving

A strong desire to explore data and ask the right questions is essential. Data scientists should be motivated by curiosity to uncover hidden insights and innovative the data solutions to business challenges. This means:

• Asking insightful questions: Proactively seeking to understand the "why" behind the data and project.
• Thinking critically: Evaluating data and results with a skeptical and analytical mindset.
• Developing creative solutions: Finding innovative ways to use data to address business needs.

Key Artefacts:

• Sprint Backlog (grooming, estimating)
• Data Sets
• Notebooks
• Models Artifacts
• Experiment Logging
• Visualizations
• Research
• Insight