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Internship Python Pandas information

What are the key skills and qualifications needed to thrive as an Internship Python Pandas, and why are they important?

To thrive in a Python Pandas internship, you need a solid understanding of Python programming, data manipulation, and familiarity with the Pandas library, often supported by coursework or personal projects in data analysis. Experience with tools such as Jupyter Notebook, NumPy, and version control systems like Git is commonly expected. Strong problem-solving skills, attention to detail, and the ability to communicate findings clearly will help you stand out. These skills and qualities are crucial for efficiently handling real-world datasets, contributing to team projects, and delivering actionable insights.

What types of projects and tasks can I expect to work on during a Python Pandas internship?

As a Python Pandas intern, you will typically work on data-driven projects such as data cleaning, transformation, analysis, and visualization. You might assist in preparing datasets for machine learning models, generating reports, or automating data workflows using Pandas and related libraries. Interns often collaborate with data scientists or analysts, gaining hands-on experience with real-world datasets and contributing to team objectives. This role offers a supportive environment to develop technical skills and learn industry best practices while making a meaningful impact.

What are Internship Python Pandas positions?

Internship Python Pandas positions are entry-level roles designed for students or recent graduates to gain hands-on experience working with Python and the Pandas library. These internships typically involve tasks like data cleaning, analysis, and manipulation using Pandas, often within the context of real-world projects. Interns may work on data-driven applications or support teams in preparing datasets for machine learning or business intelligence. These roles help interns build practical skills in data science and software development, and often serve as a stepping stone to more advanced roles in the tech industry.

What is the difference between Internship Python Pandas vs Data Analyst?

AspectInternship Python PandasData Analyst
Required SkillsPython, Pandas, basic data manipulationData analysis, SQL, Excel, visualization
Work EnvironmentInternship, entry-level, training-focusedFull-time, professional setting, project-driven
Industry UsageLearning phase, supporting data tasksInterpreting data, reporting, decision-making

Internship Python Pandas roles focus on learning and supporting data tasks using Python and Pandas, often as entry-level positions. Data Analysts have broader responsibilities, including interpreting data, creating reports, and making data-driven decisions. While both roles require some overlapping skills, Data Analysts typically have more experience and a wider skill set.

What are the most commonly searched types of Python Pandas jobs in New York? The most popular types of Python Pandas jobs in New York are:
What are popular job titles related to Internship Python Pandas jobs in New York? For Internship Python Pandas jobs in New York, the most frequently searched job titles are:
What job categories do people searching Internship Python Pandas jobs in New York look for? The top searched job categories for Internship Python Pandas jobs in New York are:
What cities in New York are hiring for Internship Python Pandas jobs? Cities in New York with the most Internship Python Pandas job openings:
Internship Python Pandas jobs near you
Real Estate Data & Automation Analyst

Real Estate Data & Automation Analyst

Stellar Management

New York, NY • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

The Real Estate Data & Automation Analyst will support the firm's Acquisitions and Asset Management groups by building and maintaining the data infrastructure, scrapers, and internal tools used to evaluate investments, manage existing assets, and respond to regulatory matters. The Analyst will sit at the intersection of the firm's deal team and its technology function, translating open-ended business questions into repeatable software-driven workflows.
Under general direction from senior leadership in Acquisitions & Asset Management, the Analyst will be expected to write production-quality code, deliver reusable internal tools, and contribute to the firm's broader effort to modernize its technology stack and integrate AI into day-to-day operations. The role provides regular exposure to Principals and senior management, and offers a holistic view of how the firm sources, underwrites, owns, and operates assets.
Build and maintain scrapers, pipelines, and internal datasets sourced from the NYC open-data ecosystem and adjacent providers, including DOB / DOB NOW, ACRIS, HPD, DOF, PLUTO, ZoLa, NYC Open Data, StreetEasy, and OCA court records.
Aggregate sales and listing comps, permits, violations, complaints, dockets, registrations, and ownership records on demand and in repeatable batches to support underwriting and asset management.
Develop screening tools that identify acquisition, conversion, and off-site opportunities by filtering on community district, ZFA, light and air, vacancy, and zoning overlays.
Translate one-off analyst requests into durable, documented tools that the broader team can reuse.
Support active regulatory and asset-management matters by tagging, aggregating, and reconciling permits, contractor invoices, IAI records, and renovation documentation across the portfolio.
Maintain organized, audit-ready libraries of permits, plans, and supporting documentation by asset and by unit.
Identify candidate workflows for AI / LLM integration - including document classification, lease abstraction, permit interpretation, and natural-language querying of internal data - and prototype and evaluate solutions.
Migrate recurring analyses away from ad-hoc Excel exports and toward versioned scripts, internal datasets, and dashboards that refresh on a schedule.
Document data sources, code, and tooling so that work persists beyond any single project or staffing change.
Coordinate with outside counsel, consultants, property management, and acquisitions team members on data and documentation requests as needed.
Other related data, automation, and analytical tasks as assigned.
Working proficiency in Python, including pandas and at least one HTTP / web-scraping library (requests, httpx, BeautifulSoup, Playwright, or similar).
Comfort with SQL and basic relational data modeling.
Familiarity with Git and standard developer practices, including version control and code review.
Strong written and verbal communication skills, with the ability to summarize technical findings for non-technical audiences across the firm.
A self-motivated, organized approach to work, with the ability to scope and execute on open-ended business questions.
Strong attention to detail and a commitment to high-quality, reproducible output.
Proficiency in MS Excel, PowerPoint, and Word.
Demonstrated interest in real estate, urban planning, housing policy, or NYC zoning and rent regulation preferred.
Exposure to GIS / geospatial tools (QGIS, PostGIS, GeoPandas) and NYC datasets such as PLUTO / MapPLUTO helpful, but not required.
Experience with LLM-based workflows, including retrieval-augmented generation, embeddings, or structured extraction, helpful, but not required.
Familiarity with Yardi or other real estate operating systems helpful, but not required.
Bachelor's degree in computer science, software engineering, data science, or a related field required.
0-2 years of relevant experience; recent graduates encouraged to apply.
Prior internship, coursework, or independent project work involving data scraping, automation, or applied machine learning preferred.
On-site presence required 5 days per week at the corporate office (44 West 28th Street, New York, NY).
Must be able to sit, stand, and walk for extended periods.
Capable of in-person, phone, and video communication with internal teams, outside counsel, vendors, and city agencies.

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