Data Scientist

Danta Technologies; San Antonio (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Healthcare insurance

Paid time off

Major holidays and sick leave

Job summary

Danta Technologies is seeking an experienced Data Scientist to lead feature selection, engineering, and statistical modeling initiatives. You will build scalable feature pipelines, validate features with rigorous statistical methods, and deploy ML models in collaboration with Data Engineering and MLOps teams.

You will work with Python, SQL, and NLP to extract meaningful features from large datasets, driving predictive analytics and business impact across complex data sources.

Qualifications

• 8+ years of experience in Data Science, Analytics, or Machine Learning.
• Strong expertise in Feature Selection, Feature Engineering, Statistical Modeling, and Predictive Modeling.
• Deep understanding of Statistics, Hypothesis Testing, Regression Analysis, Feature Importance Techniques, and Dimensionality Reduction.
• Hands-on experience in Machine Learning model development, validation, tuning, and evaluation.
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels).
• Strong SQL skills with experience writing complex queries and performing large-scale data analysis.
• Experience with NLP techniques, text processing, and feature extraction.
• Knowledge of Data Engineering concepts, ETL/ELT pipelines, data transformation, and production data workflows.
• Experience with model deployment, MLOps concepts, and collaboration with Data Engineering teams.
• Excellent analytical, problem-solving, documentation, and stakeholder communication skills.

Responsibilities

• Lead feature selection initiatives using covariance, hypothesis testing, regression, IV, WoE, PCA, and feature importance methods.
• Analyze large datasets to identify key variables influencing outcomes and predictive performance.
• Design, develop, and maintain scalable feature engineering frameworks for structured and unstructured data.
• Conduct EDA, data profiling, and statistical validation to uncover patterns and predictive features.
• Use Python and SQL to extract, transform, and validate data, ensuring data quality.
• Build, train, validate, and optimize ML models to solve business problems and improve accuracy.
• Apply ML algorithms to assess feature effectiveness and model performance.
• Leverage NLP to extract and optimize features from textual data.
• Collaborate with stakeholders to translate requirements into actionable analytical features and models.
• Build and productionize data pipelines and feature datasets with Data Engineering teams.
• Document pipelines, logic, inputs/outputs, and deployment requirements for handover to engineering.
• Support model deployment, monitoring, and continuous improvement with Data Engineering and MLOps teams.
• Document methodologies, findings, and recommendations for reusable analytics solutions.

Skills

Feature Selection

Feature Engineering

Statistical Modeling

Predictive Modeling

Python

SQL

NLP

Data Engineering

ETL/ELT

MLOps

Model Deployment

Collaborate with stakeholders

Tools

Pandas

NumPy

Scikit-learn

Statsmodels

Job description

Employment Eligibility Statement

Due to specific project and client requirements, this position is open to U.S. Citizens and U.S. Lawful Permanent Residents (Green Card holders). Sponsorship is not available at this time.

Employment Eligibility Statement

Due to specific project and client requirements, this position is open to U.S. Citizens and U.S. Lawful Permanent Residents (Green Card holders). Sponsorship is not available at this time.

Danta Technologies evaluates all candidates in compliance with the Immigration and Nationality Act (INA) and EEOC guidelines. All hiring decisions are made without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic.

Job Description:

Feature Selection, Feature Engineering, Statistical Modeling, Model Building, Machine Learning, Python, SQL, NLP, Data Engineering, ETL/ELT, Data Pipeline Development.

Key Responsibilities

• Lead feature selection initiatives using advanced statistical techniques such as correlation analysis, hypothesis testing, regression analysis, Information Value (IV), Weight of Evidence (WoE), PCA, and feature importance methods.
• Analyze large and complex datasets to identify, evaluate, and prioritize key variables that significantly influence business outcomes and predictive performance.
• Design, develop, and maintain scalable feature engineering frameworks for both structured and unstructured data sources.
• Perform exploratory data analysis (EDA), data profiling, and statistical validation to uncover meaningful patterns, relationships, and predictive features.
• Utilize Python and SQL to extract, transform, analyze, and validate data while ensuring data quality and consistency across analytical workflows.
• Build, train, validate, and optimize Machine Learning models using appropriate algorithms and techniques to solve business problems and improve predictive accuracy.
• Apply Machine Learning algorithms to assess feature effectiveness, validate feature sets, and improve model accuracy, robustness, and interpretability.
• Leverage NLP techniques to extract, engineer, and optimize features from textual data for downstream analytics and predictive modeling.
• Collaborate closely with business stakeholders, product teams, and data engineers to translate business requirements into meaningful analytical features, predictive models, and actionable insights.
• Build, optimize, and productionize data pipelines and feature datasets, working with Data Engineering teams to ensure scalability, efficiency, governance, and operational readiness.
• Conduct Data Engineering handover activities by documenting data pipelines, feature engineering logic, model inputs/outputs, transformation rules, and deployment requirements to ensure seamless transition to engineering and operations teams.
• Support model deployment, monitoring, performance tracking, and continuous improvement by partnering with Data Engineering and MLOps teams.
• Document feature selection methodologies, model development processes, statistical findings, assumptions, and recommendations while establishing best practices for reusable and automated analytics solutions.

Required Skills:

• 8+ years of experience in Data Science, Analytics, or Machine Learning.
• Strong expertise in Feature Selection, Feature Engineering, Statistical Modeling, and Predictive Modeling.
• Deep understanding of Statistics, Hypothesis Testing, Regression Analysis, Feature Importance Techniques, and Dimensionality Reduction.
• Hands-on experience in Machine Learning model development, validation, tuning, and evaluation.
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels).
• Strong SQL skills with experience writing complex queries and performing large-scale data analysis.
• Experience with NLP techniques, text processing, and feature extraction.
• Knowledge of Data Engineering concepts, ETL/ELT pipelines, data transformation, and production data workflows.
• Experience with model deployment, MLOps concepts, and collaboration with Data Engineering teams.
• Excellent analytical, problem-solving, documentation, and stakeholder communication skills.

Notes

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation or any other characteristic protected by local law, regulation, or ordinance.

Benefits

Danta offers a compensation package to all W2 employees that are competitive in the industry. It consists of competitive pay, the option to elect healthcare insurance (Dental, Medical, Vision), Major holidays and Paid sick leave as per state law.

The rate/ Salary range is dependent on numerous factors including Qualification, Experience and Location.