Data Scientist

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🕒 March 17

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Aone Talent

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2019

💼 Consulting

📣 Marketing

🎯 Recruiter

Consulting • Marketing • Recruitment

Aone Talent is a recruitment and staffing firm that connects job seekers with employers through an AI-powered platform and HR consulting services. The company provides customized job matching, candidate screening, and end-to-end recruiting and staffing solutions to businesses across industries, while also offering guidance and support to candidates. Aone Talent focuses on efficient, scalable talent acquisition and HR outsourcing to help organizations fill contingent and permanent roles.

📋 Description

• Collecting, analyzing, and interpreting large datasets to help organizations make data-driven decisions • Use statistical methods, programming, and machine learning techniques to uncover insights and build predictive models • Gather data from databases, APIs, logs, or external sources • Clean, structure, and preprocess raw data (handle missing values, outliers) • Perform exploratory data analysis (EDA) to find patterns and trends • Use statistical techniques to interpret data • Develop predictive models using techniques like regression, classification, clustering • Train, test, and tune machine learning models • Create dashboards, charts, and reports (e.g., using tools like Tableau or Python libraries) • Explain insights to non-technical stakeholders clearly • Deploy models into production environments • Monitor performance and retrain models when needed • Work with engineers, analysts, and business teams to translate business problems into data-driven solutions

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science or related field • Programming: Python, R, or SQL • Libraries: Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch • Data visualization: Matplotlib, Seaborn, Power BI, Tableau • Big data tools (optional but valuable): Spark, Hadoop • Strong understanding of Statistics & probability, Hypothesis testing, Experimental design (A/B testing) • Machine Learning Knowledge: Supervised & unsupervised learning, Feature engineering, Model evaluation metrics • Tools & Platforms: Cloud platforms: AWS, Azure, or GCP, Version control: Git • Problem-solving mindset • Communication and storytelling