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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


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.


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.


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.


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.


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.


Please click here to apply.


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