Data Analyst

Roles and responsibilities

Search: Experiment with text ads, bidding, and campaign structures on Google, Bing, Baidu, Naver, and other search engines. Adapt to new product features and roll out changes from successful tests
Display: Test, analyze, and optimize campaigns on Facebook, Twitter, Instagram, and others
Modeling: Analyze the vast amounts of data generated by experiments, develop models we can use for optimization, and build dashboards for account managers
Data Collection & Preparation:

Collect, clean, and organize data from various sources (e.g., databases, spreadsheets, APIs, and cloud storage systems).
Prepare and preprocess data for analysis, including handling missing values, data normalization, and outlier detection.
Data Analysis & Interpretation:

Perform statistical analysis and data modeling to uncover trends, patterns, and relationships within datasets.
Use statistical tests (e.g., t-tests, chi-squared, ANOVA) to validate hypotheses and ensure the accuracy of insights.
Analyze large datasets and summarize results in clear, actionable formats for stakeholders.
Data Visualization & Reporting:

Develop interactive dashboards and visualizations to represent data in a clear, compelling way for different audiences.
Create regular reports and ad-hoc analyses to present insights, trends, and patterns that inform decision-making.
Use visualization tools like Tableau, Power BI, or Matplotlib/Seaborn (in Python) to illustrate findings effectively.
Trend Identification & Forecasting:

Identify and forecast business trends by performing trend analysis and predictive modeling.
Utilize historical data to predict future outcomes and trends that inform strategic decisions.
Collaboration with Cross-Functional Teams:

Work closely with business teams (e.g., marketing, finance, operations, product development) to understand data requirements and develop tailored analytical solutions.
Collaborate with IT and engineering teams to ensure data quality, infrastructure, and reporting systems meet the analytical needs of the organization.

Desired candidate profile

Bachelor’s Degree or higher from top university in a quantitative subject (computer science, mathematics, engineering, statistics or science)
Ability to communicate fluently in English
Exposure to one or more data analysis packages or databases, e.g., SAS, R, SPSS, Python, VBA, SQL, Tableau
Good numerical reasoning skills
Proficiency in Excel
Intellectual curiosity and analytical skills
Proficiency in statistical software such as R, Python (with libraries like Pandas, NumPy, Matplotlib, Seaborn), or SAS.
Experience with SQL for data extraction, manipulation, and querying from databases like MySQL, PostgreSQL, or MS SQL Server.
Data visualization tools experience: Tableau, Power BI, or similar tools for creating interactive dashboards and reports.
Excel proficiency (advanced level) for handling large datasets, creating pivot tables, and performing complex calculations.
Knowledge of ETL processes (Extract, Transform, Load) for data cleaning and preparation.
Quantitative & Analytical Skills:

Strong foundation in statistical analysis, hypothesis testing, and data sampling methods.
Familiarity with techniques like regression analysis, predictive modeling, and time series analysis.
Ability to apply statistical and machine learning algorithms (e.g., clustering, decision trees, etc.) to draw insights from structured and unstructured data.