Remote Data Scientist — ML & Predictive Analytics

Talento Para TI; United States; Remote; USD 100,000 - 140,000; Full time

Benefits offered by this job

Health insurance

Unlimited PTO

Growth opportunities

Collaborative work environment

Work with cutting-edge technologies

Job summary

Talento Para TI is seeking a Data Scientist who thrives on data and technology to extract insights from large datasets and build production-ready predictive systems. You will apply data mining and statistical analysis to inform smarter product decisions and drive business outcomes.

The ideal candidate has a strong statistics background, proficiency in Python, and experience deploying models. Excellent communication with technical and non-technical stakeholders is essential.

Qualifications

• Strong background in statistics, mathematics, or a related quantitative field.
• Proficient in Python and data science libraries (pandas, NumPy, scikit-learn).
• Experience developing machine learning models and deploying them to production.
• Strong communication to explain complex concepts to non-technical stakeholders.

Responsibilities

• Collaborate with stakeholders to identify opportunities to leverage company data for business solutions.
• Mine and analyze data from company databases to improve product development, marketing, and strategies.
• Develop and apply custom data models and algorithms to data sets.
• Use predictive modeling to enhance customer experiences, revenue, and targeting.
• Develop processes to monitor model performance and data quality.

Skills

Statistics background

Python programming

Data storytelling/communication

Education

Bachelor's degree in a quantitative field

Tools

Pandas

NumPy

scikit-learn

TensorFlow

PyTorch

Job description

Talento Para TI is seeking a Data Scientist who thrives on data and technology to extract insights from large datasets and build production-ready predictive systems. You will apply data mining and statistical analysis to inform smarter product decisions and drive business outcomes.

The ideal candidate has a strong statistics background, proficiency in Python, and experience deploying models. Excellent communication with technical and non-technical stakeholders is essential.