Cushman & Wakefield is hiring for a fresher entry level Data Analyst role in India

Position:

Data Analyst

Company:

Cushman & Wakefield

Location:

Gurgaon, India

Job type:

Full-time

Job mode:

On-site

Job requisition id:

R276441

Years of experience:

0–3 years

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Company description 

• Cushman & Wakefield is a globally recognized name in real estate services, operating in more than 60 countries.

• With over 52,000 employees and nearly 400 offices worldwide, the company serves clients with high standards in real estate strategy, operations, and transactions.

• In 2023 alone, Cushman & Wakefield generated $9.5 billion in revenue, indicating both scale and trust in its offerings.

• The company works with building owners, occupiers, and investors to help them maximize property value through tailored solutions.

• Cushman & Wakefield is publicly traded on the New York Stock Exchange under the ticker symbol CWK.

• Their work spans multiple real estate domains, including asset services, capital markets, facility services, global occupier services, leasing, and valuation.

• The organization is driven by innovation, client satisfaction, and delivering measurable business impact through customized solutions.

• The company fosters an inclusive workplace, encouraging growth, diversity, and a performance-driven culture.

• They actively invest in cutting-edge technology to improve data analytics, sustainability, and operational efficiency.

• Their long-term strategy includes strengthening service lines and digital transformation for deeper market insights and better client support.

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Profile overview: 

• The Data Analyst will collaborate closely with program managers and department leaders to gather analytics requirements and deliver meaningful insights.

• The role requires managing key performance indicators (KPIs), identifying trends, and providing strategic data-driven suggestions.

• You will be expected to build intuitive dashboards and data visualizations that help stakeholders make informed decisions.

• The position will involve supporting revenue growth by enabling upsell and cross-sell strategies through targeted data analysis.

• Your analysis will include deep dives into operational inefficiencies and productivity gaps across business functions.

• You’ll be responsible for maintaining and improving data pipelines, sourcing from both primary and secondary sources.

• Strong understanding of system architecture and integration methods is important for scalable solution delivery.

• The role will demand skills in predictive analytics, including churn analysis, to impact both client retention and top-line growth.

• You will ensure data consistency and reliability through data cleansing and mining methodologies.

• This profile requires a proactive and detail-oriented mindset that can translate raw data into actionable strategies aligned with the company’s broader objectives.

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Qualifications: 

• Basic knowledge of data analytics principles and tools is required, especially Excel, including pivot tables and chart creation.

• Exposure to BI tools like Power BI, Tableau, or Looker is a plus.

• Experience or coursework related to SQL and relational databases is beneficial.

• Understanding of KPIs and their role in business monitoring and optimization is useful.

• Some experience with data integration processes or familiarity with tools like Alteryx, Apache NiFi, or Python-based ETL pipelines is desirable.

• Candidates should demonstrate the ability to perform exploratory data analysis (EDA) and communicate findings with clarity.

• You should have a curious mindset with the ability to ask business-relevant questions and pursue answers independently.

• Attention to detail is critical, especially when handling large datasets or conducting predictive modeling.

• Strong collaboration skills are needed to work across teams and translate technical findings into simple, strategic insights.

• Familiarity with churn analysis, customer segmentation, or basic machine learning techniques (regression, clustering) will be advantageous but is not mandatory.

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Additional info: 

• The job is based in Gurgaon, India, and requires physical presence at the office.

• It is a full-time position with no mention of contract limitations, suggesting a permanent opportunity.

• This role is suited for candidates who are in the early stages of their analytics career or those looking to transition into the field.

• Applicants with up to 3 years of experience will be considered; freshers are welcome to apply.

• The selected candidate will have the opportunity to contribute to high-impact projects supporting global operations.

• Cushman & Wakefield offers a collaborative work culture, emphasizing mentorship and skill development.

• Exposure to real estate data analytics will be a distinctive aspect of this role.

• You will learn how data drives key business functions, especially in property valuation, client management, and operational efficiencies.

• The team environment encourages regular knowledge-sharing, project collaboration, and cross-functional engagement.

• You’ll also gain practical experience in creating insights that influence business direction and client solutions.

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Detailed Role Responsibilities 

Business Collaboration and Stakeholder Alignment

• Act as a liaison between the data team and business units by translating stakeholder requirements into measurable data strategies.

• Regularly meet with program managers, department heads, and other key stakeholders to identify analytics gaps and align goals.

• Support decision-making by identifying which metrics directly relate to business performance and designing corresponding KPIs.

• Maintain an understanding of business goals and adapt analytics delivery to meet evolving needs.

• Provide customized reporting and insights on a regular basis, tailoring views depending on the audience (executive, operational, technical).

Data Mining and Exploration

• Perform exploratory data analysis (EDA) to understand trends, distributions, and anomalies within datasets.

• Use advanced filtering and transformation techniques to clean raw data and prepare it for further analysis.

• Mine structured and unstructured data from internal databases as well as external sources to identify business trends.

• Assist in connecting data from different departments for holistic, cross-functional views that support better strategic planning.

• Build dashboards that allow real-time data exploration and pattern recognition.

Predictive Modeling and Advanced Analytics

• Design and develop predictive models to identify potential opportunities or risks across the customer lifecycle.

• Implement churn prediction models using customer usage patterns and other behavioral indicators.

• Explore regression models to understand the factors driving revenue, cost, or service quality metrics.

• Use clustering techniques to segment customer or project data into meaningful categories for improved targeting.

• Collaborate with data scientists or consultants, if required, to validate model outputs and recommendations.

Reporting and Visualization

• Create intuitive dashboards and static reports using tools like Excel, Tableau, or Power BI.

• Present visualizations that combine multiple variables and KPIs into digestible, business-friendly formats.

• Automate recurring reports for various business teams, including sales, finance, HR, and operations.

• Highlight actionable insights using heatmaps, bar charts, waterfall diagrams, and other appropriate techniques.

• Continuously improve reporting mechanisms based on user feedback or changes in business needs.

Data Infrastructure and Integration

• Acquire data from primary and secondary sources such as ERP systems, APIs, Excel sheets, and web-based forms.

• Write SQL queries or use no-code tools to pull relevant data sets from various systems.

• Ensure data consistency across platforms by applying normalization techniques and validation rules.

• Support efforts to integrate new data sources into the analytics framework, especially as new tools or departments come online.

• Work with IT or infrastructure teams to ensure database security, version control, and compliance.

Technical Expertise and Tools

• Maintain proficiency in spreadsheet tools like Microsoft Excel or Google Sheets, including functions, pivot tables, and advanced formulas.

• Use scripting (e.g., SQL or Python) to perform ad hoc analysis or automate recurring data tasks.

• Understand how different data storage structures (relational, NoSQL, cloud) affect data querying and analysis.

• Participate in workshops or training to improve technical knowledge in analytics and data handling.

• Identify opportunities to reduce manual work through automation and smarter data structuring.

Impact on Business Performance

• Analyze operational bottlenecks and recommend improvements to increase efficiency.

• Identify potential cross-selling or upselling opportunities by analyzing client usage patterns and account behaviors.

• Measure the success of marketing campaigns, product launches, or customer success initiatives through historical data.

• Track and optimize workforce performance or departmental efficiency using time-series and comparative data.

• Recommend changes in business processes based on data trends and correlations observed over time.

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