Data Scientist

NeuroHire

. Collect, clean, process, and analyze large datasets from multiple sources.

Posted 11/7/2026full-timeRemote • United States, CanadaMid-LevelSenior💰 $80,000 - $100,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data collection, cleaning, and analysis, with proficiency in Python and SQL for developing machine learning models and data visualizations. Strong analytical skills and ability to communicate complex findings effectively.

Highest-signal resume keywords

Python ProgrammingMachine Learning Model DevelopmentSQL ProficiencyData VisualizationStatistical Analysis

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data CleaningExploratory Data AnalysisFeature EngineeringRegression AnalysisClassificationClusteringModel EvaluationStatistical TechniquesMathematical FundamentalsSupervised Learning

Soft Skills

Analytical ThinkingProblem-SolvingAttention to DetailTeamworkCommunication

Tools & Technologies

PandasNumPyScikit-learnMatplotlibJupyter NotebookGitGitHubTensorFlowPyTorchAWS

Certifications & Qualifications

Master's Degree in Data ScienceMaster's Degree in Computer ScienceMaster's Degree in StatisticsMaster's Degree in MathematicsMaster's Degree in Artificial IntelligenceMaster's Degree in Machine Learning

Industry Keywords

Data ScienceMachine LearningData AnalyticsPredictive AnalyticsData ProcessingModel DeploymentAPIsMachine Learning PipelinesNLPGenerative AI

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Collect, clean, process, and analyze large datasets from multiple sources.
Perform exploratory data analysis to identify distributions, relationships, trends, and business opportunities.
Develop, train, test, and evaluate machine learning models.
Apply statistical techniques and mathematical methods to support evidence-based decisions.
Write efficient Python code using Pandas, NumPy, Scikit-learn, and Matplotlib.
Build predictive, classification, regression, clustering, and other machine learning solutions.
Use SQL databases to extract, manipulate, and analyze structured data.
Create data visualizations, reports, and presentations communicating analytical findings.
Evaluate model performance and recommend improvements.
Collaborate with software engineers, data analysts, and cross-functional teams to translate business requirements into analytical solutions.
Assist with data processing and machine learning workflows.
Maintain documentation of datasets, methodologies, experiments, and model results.
Stay updated on data science techniques, machine learning technologies, and industry best practices.

Requirements

What you’ll need

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 Pandas, NumPy, and Scikit-learn.
Solid understanding of statistics, probability, linear algebra, and mathematical fundamentals.
Familiarity with supervised and unsupervised learning.
Understanding of data preprocessing, data cleaning, feature engineering, and exploratory data analysis.
Proficiency in SQL for querying, joining, filtering, and analyzing datasets.
Familiarity with Matplotlib, Seaborn, or Plotly.
Understanding of 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.
Preferred: academic projects, internships, or personal projects involving data science, machine learning, or predictive analytics.
Preferred: familiarity with TensorFlow or PyTorch.
Preferred: basic understanding of NLP and generative AI concepts.
Preferred: exposure to AWS, Microsoft Azure, or Google Cloud Platform.
Preferred: familiarity with model deployment, APIs, or machine learning pipelines.
Preferred: experience participating in data science competitions or working with real-world datasets.

Benefits

Comp & perks

Opportunity to contribute to an innovative, AI-powered technology company.
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 data-driven solutions and meaningful business initiatives.