Data Science Intern presso Shyftlabs

Position OverviewAt ShyftLabs, we live and breathe data and we want you to, too. We help Fortune 500 companies unlock massive growth with cutting-edge digital solutions. We're not just looking for interns, we're looking for the next generation of data innovators. If you're passionate about applying data-driven thinking to real-world challenges, this opportunity is for you. As part of our team, you’ll work closely with experienced data scientists and engineers to uncover insights, build predictive models, and contribute to optimizing the performance and efficiency of our products.This internship will last between 4 - 12 months, offering hands-on experience in analytics, experimentation, and applied machine learning within a fast-paced data product company.

What you'll be doing

Data Analysis & Research: Analyze large and complex datasets using SQL, Python, or R to uncover insights, identify trends, and support data-driven business and product decisions.

Modeling & Simulation: Conduct exploratory data analysis, build models, and run simulations to validate hypotheses and quantify potential outcomes.

Experimentation & Causal Inference: Assist in designing and analyzing experiments to measure performance, determine causality, and generate actionable recommendations.

Cross-Functional Collaboration: Partner with engineers, product managers, and other stakeholders to translate data findings into impactful strategies and solutions.

Reporting & Visualization: Communicate insights effectively through dashboards, reports, and visual presentations that enable clear understanding and decision-making.

What you bring

Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.

Strong analytical and problem-solving skills with a curious, data-driven mindset.

Solid understanding of statistics, hypothesis testing, and basic machine learning techniques (e.g., regression, classification, clustering).

Hands-on experience with Python (Pandas, NumPy, Scikit-learn) and/or R, plus familiarity with SQL for data manipulation and analysis.

Excellent communication and collaboration skills, with the ability to work effectively in a team environment.

Nice to have

Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Exposure to big data tools such as Spark, Databricks, or Delta Lake.

Experience from academic or personal projects related to data science, analytics, or machine learning.

Understanding of experimental design, A/B testing, or causal inference.

Interest in AI infrastructure and data-driven product development.

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