Applied Data Scientist

Applied Data Scientist at Built In NYC employer listing — Research Report

Introduction

The Applied Data Scientist role at Built In NYC employer listing is a high-impact position that bridges advanced analytics with real business solutions. This internship offers a unique opportunity to work on AI initiatives while supporting data-driven decisions across the organization. The deadline for application is not mentioned, so candidates should apply immediately to secure a spot. Working here provides career benefits including exposure to cutting-edge tools, mentorship from industry experts, and a pathway to full-time employment in the tech sector.

Overview of Built In NYC employer listing

Built In NYC employer listing is a leading platform that connects top talent with innovative companies in New York City. The platform operates within the tech recruitment industry, competing with sites like LinkedIn and Glassdoor while offering a niche focus on NYC-based employers. Key services include detailed company profiles, job listings, and insider reviews that help job seekers make informed decisions. The platform has significant market presence, growing rapidly as more companies and candidates rely on its data-driven insights. The work environment is collaborative, transparent, and reputation-focused, with a culture that values innovation and integrity. People want to work here because it offers direct access to the best tech jobs in NYC and a community of forward-thinking professionals.

Applied Data Scientist Role

Role Overview

An Applied Data Scientist at Built In NYC employer listing supports AI initiatives by managing data, performing exploratory analysis, and building AI-enabled models. The intern or employee impacts the product by enabling smart, data-driven decisions that improve the platform's functionality and user experience. This role is critical for maintaining the accuracy and relevance of job listings and company data on the platform.

Detailed Responsibilities

• Collect, clean, and organize data from various sources to support AI analytics and modeling.
• Assist team members in exploratory data analysis, feature engineering, and segmentation using AI tools.
• Help build, document, and maintain AI-enabled models, workflows, and data assets.
• Analyze trends, patterns, and predictions generated by AI models to recommend actionable insights.
• Create visualizations and interactive dashboards using Excel, Tableau, Power BI, or Python scripting.
• Document methodologies, processes, and results to ensure reproducibility and compliance.
• Present findings and recommendations from AI-backed analyses to team leads and cross-functional stakeholders.
• Collaborate with data scientists, AI specialists, and business teams to clarify analysis requirements.

Day-to-Day Workflow

A typical day starts with reviewing data pipelines and cleaning new datasets to ensure quality. The scientist then moves to exploratory analysis, identifying patterns that could improve model performance. Midday involves building or refining AI models, often using Python and SQL, followed by creating visualizations to communicate findings. The afternoon is dedicated to collaborating with cross-functional teams to clarify requirements and present insights. The workflow is dynamic, balancing technical tasks with communication and strategic planning.

Tools and Technologies

The tech stack includes Python, SQL, and R for data processing and analysis. Visualization tools like Tableau, Power BI, and Excel are used for dashboards. AI frameworks and machine learning libraries such as scikit-learn and TensorFlow support model building. The platform also leverages cloud services for data storage and computation, ensuring scalability and efficiency.

Skills and Requirements

Technical Skills

Candidates must have proficiency in statistical modeling, machine learning concepts, and data processing. Essential tools include Python, SQL, R, and Excel. Familiarity with artificial intelligence principles, data science methodologies, and predictive analytics is a strong plus. Deep knowledge of data visualization and the ability to manage unstructured data are also critical for success in this role.

Soft Skills

Strong analytical and critical thinking skills are required to solve complex data problems. Communication skills must be excellent to present findings to non-technical stakeholders. Teamwork is essential for collaborating with data scientists and business teams. Intellectual curiosity and motivation to innovate drive continuous improvement in data processes and model performance.

Experience Expectations

Candidates should be currently enrolled in a Bachelor’s or Master’s program in Data Science, Artificial Intelligence, Statistics, Computer Science, or Economics. A portfolio showcasing data projects, feature engineering, or model building is highly valued. While a graduate degree is not required, candidates with relevant coursework or internships in data science are prioritized. GPA expectations are flexible, but a strong academic record in quantitative fields is preferred.

Salary and Benefits

The pay range for the Applied Data Scientist internship is $20/hr to $34/hr, depending on skills and experience. Full-time Applied Science Intern roles in similar markets offer USD $129,000 to $213,000 annually. Perks include a discretionary annual incentive program, comprehensive benefits, and learning budgets for courses or boot camps. The role is remote in New York, NY, offering flexibility. Full-time conversion opportunities are available for high-performing interns, providing a clear path to a permanent career.

Built In NYC employer listing Hiring Process

Step-by-Step Hiring Stages

1. Application: Submit resume, portfolio, and cover letter via the Built In NYC platform.
2. Screening: ATS filters for keywords like Python, SQL, and machine learning.
3. Assignment: Complete a data analysis project or case study.
4. Interviews: Technical and behavioral interviews with data scientists and hiring managers.
5. Offer: Receive a formal offer with salary and benefits details.

Application Timeline

Candidates should apply as soon as possible since the deadline is not mentioned. The typical process duration is 2–4 weeks, from application to offer. Early applicants have a higher chance of securing a spot due to limited internship capacity.

Screening Methods

The ATS scans for keywords such as Python, SQL, machine learning, and data visualization. A portfolio with real-world data projects is required to demonstrate practical skills. Resumes must highlight relevant coursework, internships, and technical achievements to pass the initial screening.

Interview Preparation

Example Interview Questions

• How would you clean a dataset with missing values and outliers for AI modeling?
• Explain a machine learning project you built and the impact it had on the business.
• What tools do you use for data visualization, and why do you prefer them?
• How do you collaborate with non-technical teams to clarify data requirements?

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure answers. For technical questions, explain your thought process clearly, detailing the tools and algorithms chosen. For behavioral questions, highlight teamwork and communication skills with specific examples. Always connect your answers to the business impact of your work.

What Recruiters Evaluate

Recruiters evaluate technical proficiency in Python, SQL, and machine learning. They assess problem-solving skills through case studies and project discussions. Communication clarity and the ability to explain complex concepts to non-technical audiences are critical. Cultural fit, including curiosity and motivation to innovate, is also a key hiring criterion.

How to Get Selected

Practical Tips

• Tailor your resume to highlight keywords like Python, SQL, and machine learning.
• Build a portfolio with 2–3 data projects showcasing feature engineering and model building.
• Network with current employees on LinkedIn to gain insider insights.
• Apply early since the deadline is not mentioned and spots are limited.

Common Mistakes to Avoid

• Submitting a generic resume without relevant keywords or technical details.
• Ignoring the portfolio requirement, which is essential for demonstrating skills.
• Failing to explain the business impact of your data projects in interviews.
• Applying late, which reduces the chance of securing an internship spot.

How to Stand Out

Advanced strategies include creating a GitHub repository with clean, documented code for your data projects. Attend data science meetups in NYC to build connections and learn about industry trends. Submit a tailored cover letter that explains your passion for AI and data science. Highlight any internships or coursework in predictive analytics to show relevant experience.

Final Thoughts

The Applied Data Scientist internship at Built In NYC employer listing is a transformative opportunity for aspiring data professionals. With access to cutting-edge tools, mentorship, and a pathway to full-time employment, this role sets the stage for a successful career in tech. Apply now to secure your spot and start building the future of AI-driven data solutions.

Frequently Asked Questions

Q: What is the salary for Applied Data Scientist at Built In NYC employer listing? A: The internship pay range is $20/hr to $34/hr, with full-time roles offering USD $129,000 to $213,000 annually.

Q: How competitive is it to get hired at Built In NYC employer listing? A: The role is competitive due to limited spots and high demand for data science talent, but early applicants with strong portfolios have a higher chance.

Q: What skills are most important for this role? A: Proficiency in Python, SQL, machine learning, and data visualization is critical, along with strong analytical and communication skills.