Data Science, Intern

As a Data Science Intern at Tatari, you will be engaged in solving real-time optimization challenges using machine learning. Your day-to-day activities will involve exploring and refining algorithms for potential new business expansions, automating data tasks, and delivering ad-hoc analyses to reveal crucial insights. Collaborating with senior team members, you will be responsible for helping optimize Key Performance Indicators and create innovative data solutions.

To qualify for the Data Science Intern role at Tatari, applicants must be currently enrolled in an undergraduate program in fields such as Data Science, Statistics, or Computer Science. It is important that you have completed at least two years of study and not graduated by the Summer of 2025. Additionally, you should possess a strong interest in analytics and data manipulation skills in software like SQL or Python.

In this role, Data Science Interns at Tatari will work on a variety of projects including optimizing real-time issues with machine learning techniques, automating data-driven tasks, and refining algorithms to identify new business opportunities. You'll also engage in deep-dive quantitative analyses to forecast trends, all while creating innovative applications that benefit the entire team.

While familiarity with data manipulation tools like Python and SQL is essential, a strong strategic thinking capability is equally important for a Data Science Intern at Tatari. We also value quick learners who can adapt to a fast-paced environment. Any prior exposure to statistical methods could be beneficial, as well as a proactive approach to solving data-related challenges.

Interns at Tatari will be guided and mentored by experienced Senior Data Scientists and Analysts who are committed to providing valuable learning experiences. This relationship will allow you to develop technical skills in data science, gain insights into the industry, and have the autonomy to explore solutions while receiving constructive feedback throughout the internship.

When faced with a complex data problem as a Data Science Intern, I start by breaking down the problem into smaller, manageable parts. First, I identify the objectives and key metrics to focus on. I then explore the data set to gather insights and understand the underlying patterns. From there, I apply relevant analytical techniques and algorithms, continually refining my approach based on feedback and results.

Certainly! I am familiar with linear regression, which is a machine learning algorithm used to model the relationship between a dependent variable and one or more independent variables. It's particularly useful for regression tasks where the goal is to predict outcomes. I appreciate how it provides insights into how changes in predictors affect the predicted outcomes, making it valuable for data-driven decision-making.

In my academic projects, I have primarily used Python and libraries such as Pandas for data manipulation and analysis. Additionally, I have experience with SQL for database querying and visualization tools like Matplotlib for representing data effectively. Each tool has its strengths, and I choose them based on the specific needs of the analysis or project.

I believe that addressing missing or incomplete data is crucial for any analysis. I typically start by investigating the nature of the missing data—whether it's random or biased. I can then choose appropriate methods for handling it, such as imputation, removing affected variables, or using algorithms that accommodate missing values, ensuring the integrity of my analyses.

In a recent project, I had to familiarize myself with Tableau within a short timeframe to create data visualizations. I approached this challenge by accessing online tutorials, practicing with sample data, and documenting my learning process. With focused effort, I was able to produce actionable visualizations that impressed my team—and I continue to use Tableau in my work today.

When managing multiple projects, I prioritize tasks by evaluating deadlines, the complexity of the tasks, and their impact on overall goals. I also utilize project management tools to keep track of progress and set milestones, which helps me stay organized and ensures that I allocate sufficient time for each task while remaining adaptable to shifting priorities.

In the realm of TV advertising, important KPIs would include reach (the number of unique viewers), frequency (the number of times the audience is exposed to the advertisement), conversion rates, and return on investment (ROI). Understanding these metrics helps to evaluate the effectiveness of advertising campaigns and inform future strategies.

When communicating complex data findings, I focus on simplifying the information without losing its essence. I use visual aids like graphs or charts and avoid technical jargon. I relate the findings back to business objectives and explain the implications in straightforward terms, ensuring that stakeholders understand the relevance and significance of the data.

In my last project, I conducted a data analysis to identify trends in customer purchase behavior using historical sales data. I used Python for data cleaning and manipulation, then applied regression analysis to draw insights. Presenting my findings in a report and to my classmates helped me hone my ability to convey technical information clearly.

I'm particularly drawn to Tatari because of its innovative approach to TV advertising and its commitment to leveraging data analytics in such a transformative way. The chance to work with seasoned professionals and assist in pioneering efforts in the ad tech space aligns with my passion for data science. I am excited about the opportunity to contribute to your mission and learn from a talented team!