Why Work at Lenovo

We are Lenovo. We do what we say. We own what we do. We WOW our customers.

Lenovo is a US$57 billion revenue global technology powerhouse, ranked #248 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world's largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo's continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

• Data Collection & Wrangling: Gathering data from various sources (databases, APIs, web scraping, etc.), cleaning it, and transforming it into a usable format. This often involves dealing with missing values, inconsistencies, and different data types.
• Exploratory Data Analysis (EDA): Uncovering patterns, trends, and relationships in data through summary statistics, visualizations, and data mining techniques. This helps to form hypotheses and identify areas for further investigation.
• Statistical Analysis: Applying statistical methods to test hypotheses, make predictions, and draw inferences from data. This can involve techniques like regression analysis, hypothesis testing, and time series analysis.
• Data Visualization: Creating meaningful charts, graphs, and dashboards to communicate insights effectively to stakeholders. This involves choosing the right visualization techniques to tell a compelling story with the data.
• Reporting & Communication: Presenting findings and recommendations to both technical and non-technical audiences through reports, presentations, and dashboards. Strong communication skills are essential to convey complex information clearly.
• Predictive Modelling: Building models to forecast future outcomes based on historical data. This can involve machine learning techniques like regression, classification, and clustering.
• Data Management: Contributing to the development and maintenance of databases and data pipelines. This ensures data quality and accessibility for analysis.
• Collaboration: Working with cross-functional teams (e.g., marketing, product, finance) to understand their data needs and provide data-driven solutions.

• Min. 2-3 years of working experience, preferably in the SI environment.
• Education: A bachelor's degree in a quantitative field like statistics, mathematics, computer science, economics, or a related field is typically required.
• Technical Skills:
  • Programming Languages: Proficiency in at least one data analysis language like Python (with libraries like Pandas, NumPy, Scikit-learn) or R.
  • Data Wrangling Tools: SQL for querying databases, and potentially experience with tools like Excel or data manipulation libraries.
  • Data Visualization Tools: Experience with tools like Tableau, Power BI, Qlik Sense, or data visualization libraries in Python (e.g., Matplotlib, Seaborn).
  • Statistical Software: Familiarity with statistical software packages like SPSS or SAS can be beneficial.
  • Big Data Technologies: Knowledge of Hadoop, Spark, or other big data technologies may be required for roles dealing with large datasets.
• Analytical Skills: Strong analytical and problem-solving skills are crucial for interpreting complex data and drawing meaningful insights.
• Communication Skills: Excellent written and verbal communication skills are necessary to present findings clearly and concisely to both technical and non-technical audiences.
• Domain Knowledge: Depending on the industry, having domain-specific knowledge (e.g., IT and finance) can be a significant advantage.
• Soft Skills:
  • Curiosity: A natural inclination to explore data and ask questions.
  • Attention to Detail: Accuracy is essential in data analysis, so strong attention to detail is a must.
  • Critical Thinking: The ability to evaluate data objectively and draw logical conclusions.
  • Problem-Solving: The ability to identify and solve problems using data-driven approaches.