Search for Summer Data Scientist Associate roles that are remote, require proficiency in Python and SQL, target candidates pursuing a Master's or Ph.D. in Data Science or related fields, and offer full-time positions based in New Orleans, LA.

Okay, I've searched for Summer Data Scientist Associate roles that are remote, require proficiency in Python and SQL, target candidates pursuing a Master's or Ph.D. in Data Science or related fields, and offer full-time positions based in New Orleans, LA. I have found 27 positions matching your criteria.

Summer Data Scientist Associate

Remote

Python

SQL

Full Time

Can you help me to analyze the job description and company culture?

Sure. Here's the analysis:

Job Analysis:

The Summer Data Scientist Associate role is designed as an immersive, hands-on opportunity for early-stage graduate students (Ph.D. or Master's) to contribute meaningfully to Anywhere Real Estate's data-driven decision-making. Fundamentally, this role exists to transform large and complex datasets into actionable insights and predictive models that can support strategic business initiatives in real estate—a sector increasingly reliant on sophisticated analytics for competitive advantage. The job entails a full spectrum of data science work including data exploration, ETL pipelines, feature engineering, statistical analysis, and machine learning, all within a collaborative team environment. Proficiency in Python and SQL is critical not just for coding basics but to enable reproducibility, scalability, and the transition of models from research to production. The candidate will face challenges such as navigating noisy real estate data, aligning analytical findings with business goals, and communicating complex results to non-technical stakeholders, which requires both technical finesse and strong interpersonal skills. Success in this role looks like delivering robust models that influence business strategies, fostering cross-functional collaboration, and demonstrating adaptability to evolving technological tools and business questions. The emphasis on familiarization with the IT infrastructure further suggests the company values a pragmatic approach that connects analytical work to operational deployment, signaling that autonomy and understanding of real-world constraints are vital.

Company Analysis:

Anywhere Real Estate is a powerhouse in the global real estate sector, boasting a portfolio of esteemed brands that collectively create a complex, multifaceted business operating at scale (close to a million home sale transactions annually across over 118 countries). This positions the company as a well-established leader with a robust footprint but also one that must continuously innovate to maintain competitiveness internationally. The company’s culture appears people-centric, innovative, and collaborative, fostering empowerment and growth—qualities that align well with data scientists who need to thrive in cross-functional, fast-evolving environments. The mention of awards like 'Great Place to Work' and 'World’s Most Ethical Companies' underscores an ethical, inclusive, and transparent ethos that may attract candidates who value integrity and diversity. Given the company’s strategic focus on integrating data science within real estate operations, this role aligns tightly with their mission to empower customers’ next moves by enhancing the technological backbone. The data science team likely has strong visibility within the organization, interfacing with multiple business units, so this associate can expect a chance to impact decisions at various levels and collaborate with diverse teams. Strategically, this internship role supports a growth and innovation agenda, nurturing talent to advance the company’s futuristic, tech-enabled vision of real estate.

Mock up some interview questions based on the job description.

Absolutely. Here are some mock interview questions that could come up:

Behavioral: Tell me about a time when you had to analyze a large, complex dataset to solve a problem. How did you approach the task and what was the outcome?
Behavioral: Describe an experience where you had to communicate technical findings to a non-technical audience. How did you ensure they understood the key insights?
Behavioral: Give an example of when you had to learn a new technology or tool quickly to complete a project. How did you manage that process?
Behavioral: Share an instance where you collaborated with a team on a data science project. What role did you play and how did you handle any conflicts or disagreements?
Technical: Can you walk me through the steps you would take to build an end-to-end machine learning model starting from raw data?
Technical: How do you apply feature engineering in your projects? Can you provide an example where it significantly improved your model's performance?
Technical: Explain the differences between Random Forest and Gradient Boosting models. When would you prefer one over the other?
Technical: Describe your experience with ETL pipelines. What challenges have you faced in implementing these and how did you overcome them?
Situational: Imagine you completed a model with promising accuracy, but the business stakeholders don’t trust the results. How would you address their concerns?
Situational: You discover inconsistencies in the data that may affect model outcomes. What steps would you take to validate and handle this data issue?
Situational: Suppose you are tasked with explaining your model’s predictions to a product team unfamiliar with data science. How would you prepare and present your findings?
Company Fit: Why are you interested in working at Anywhere Real Estate, and how do you see your background contributing to our mission?
Company Fit: How do our company values of empowerment, innovation, and collaboration resonate with your own work style and goals?
Questions for Interviewer: Can you describe how the data science team collaborates with other departments at Anywhere Real Estate?
Questions for Interviewer: What are the biggest data challenges Anywhere Real Estate is currently tackling, and how would a summer associate contribute?
Questions for Interviewer: How does Anywhere support ongoing learning and professional growth for interns and early-career data scientists?

Job Details

Overview Company

Data Science Associate

Lensa

3 months ago

Highlight

RemoteFull-timeEntry

Summary

Anywhere Real Estate is seeking a Summer Data Science Associate to join their team remotely. The role involves working closely with the data science team to address business problems through data analysis and machine learning.

About the Role

As a Summer Data Science Associate, you will assist in exploring and understanding data, perform ETL processes, and write Python code for visualizations and model implementation. You will collaborate with other data scientists, communicate results to non-technical stakeholders, and understand IT infrastructure for model deployment.

About You

Required:

Currently pursuing a PH.D (1-2 years in the program) or Masters in Statistics, Data Science, Mathematics, Economics, or a related analytical discipline.
Proficiency in SQL and Python programming languages.
Basic understanding of statistical knowledge and machine learning algorithms.
Effective communication skills.

Preferred:

Some experience, including internships, in building end-to-end models in Python.
Exposure to deep learning.
Familiarity with techniques such as Linear Regression, GLM, Random Forest, GBM, XGboost, SVM, and various segmentation methods.
Knowledge of Neural Networks and proficiency in Python packages like TensorFlow.

Lensa

What if we could take the search out of job search? This is the question Lensa has been answering since launching in San Francisco, California in August 2016. Using machine learning, we instantly provide companies with candidates and match professionals with positions that fit their skills, goals, personalities, and needs. How does it work? Lensa applies complex algorithms that identify the skills needed to succeed on a given job. Candidates can play computer games that assess their work style, thus gaining insight into their strengths while unlocking career opportunities. The result: job search that puts people first while reducing the time and cost of talent acquisition.

Lensa passed 10 million job seeker registrations in 2019, making the com- pany one of the premier career platforms.

With teams in multiple U.S. locations and a 60-strong crew of developers and data scientists at its R & D headquarters in Europe, Lensa is working every day to share its transformative career technology with the world.

New Orleans, LA