Data Scientist

neurohire.ai

6h • $80,000 - $100,000

About The Position

NeuroHire is seeking a motivated and analytical Data Scientist to work with structured and unstructured datasets, discover meaningful patterns, develop predictive models, and generate data-driven insights that support business objectives and strategic decision-making. The role involves collaborating with technical and business teams to explore data, build machine learning models, evaluate analytical results, and turn complex datasets into actionable solutions. It is described as suitable for an early-career professional passionate about statistics, programming, machine learning, and solving real-world problems through data.

Requirements

• Master's degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related technical field.
• 0–1 year of experience in Data Science, Machine Learning, Data Analytics, or a related technical role.
• Strong programming knowledge in Python and familiarity with libraries such as Pandas, NumPy, and Scikit-learn.
• Solid understanding of statistics, probability, linear algebra, and mathematical fundamentals.
• Familiarity with supervised and unsupervised machine learning concepts.
• Understanding of data preprocessing, data cleaning, feature engineering, and exploratory data analysis.
• Proficiency in SQL for querying, joining, filtering, and analyzing datasets.
• Familiarity with visualization tools and libraries such as Matplotlib, Seaborn, or Plotly.
• Understanding of machine learning algorithms such as regression, decision trees, random forests, clustering, and classification.
• Familiarity with Jupyter Notebook, Google Colab, or similar development environments.
• Basic understanding of model evaluation, validation techniques, and performance metrics.
• Familiarity with Git and GitHub for version control and collaborative development.
• Strong analytical thinking, critical reasoning, and problem-solving abilities.
• Ability to interpret complex data and communicate findings clearly.
• Strong attention to detail, teamwork, and willingness to learn new technologies.

Nice To Haves

• Academic projects, internships, or personal projects involving data science, machine learning, or predictive analytics.
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Basic understanding of Natural Language Processing and generative AI concepts.
• Exposure to cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Familiarity with model deployment, APIs, or machine learning pipelines.
• Experience participating in data science competitions or working with real-world datasets.

Responsibilities

• Collect, clean, process, and analyze large datasets from multiple sources to identify patterns, trends, and meaningful insights.
• Perform exploratory data analysis to understand data distributions, relationships, and potential business opportunities.
• Develop, train, test, and evaluate machine learning models for analytical and predictive problems.
• Apply statistical techniques and mathematical methods to interpret data and support evidence-based decisions.
• Write efficient code using Python and relevant data science libraries, including Pandas, NumPy, Scikit-learn, and Matplotlib.
• Build predictive models, classification algorithms, regression models, and other machine learning solutions.
• Use SQL databases to extract, manipulate, and analyze structured data.
• Create visualizations, reports, and presentations to communicate analytical findings.
• Evaluate model performance using appropriate metrics and recommend improvements.
• Collaborate with software engineers, data analysts, and cross-functional teams to translate business requirements into analytical solutions.
• Assist in developing and improving data processing and machine learning workflows.
• Document datasets, analytical methodologies, experiments, and model results.
• Stay updated with emerging data science techniques, machine learning technologies, and industry best practices.

Benefits

• Hands-on exposure to real-world data science and machine learning projects.
• Opportunities to strengthen programming, statistical analysis, and machine learning skills.
• Collaborative environment that encourages experimentation, innovation, and continuous learning.
• Opportunities for professional development and technical career growth.
• Exposure to modern data science tools, technologies, and analytical workflows.
• Opportunity to contribute to an innovative, AI-powered technology company and to data-driven solutions and meaningful business initiatives.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level