Data Engineer / Data Scientist

Orison Solutions LLC; Indianapolis (IN); On-site; USD 90,000 - 150,000; Full time

Job summary

Orison Solutions LLC in Indianapolis is seeking a Data Engineer / Data Scientist to design, build, and maintain data pipelines, perform data analysis, and develop predictive models to fuel data-driven decisions.

You will collaborate with business stakeholders, apply ML techniques, ensure data quality, and work with cloud platforms (Azure, AWS, GCP) across data engineering and AI initiatives.

Qualifications

• Proficiency in Python or R for data engineering and modeling.
• Strong foundation in statistics and ML algorithms.
• Experience with data preprocessing, feature engineering, and model validation.

Responsibilities

• Design, build, and optimize scalable data pipelines and ETL processes.
• Develop and maintain data models, data marts, and analytical datasets.
• Collaborate with cross-functional teams to gather requirements and deliver data-driven solutions.
• Perform exploratory data analysis (EDA) and create machine learning models as required.
• Implement data quality checks, validation rules, and monitoring processes.
• Automate data workflows and ensure timely availability of data.
• Work with cloud platforms such as Azure, AWS, or GCP for data engineering activities.
• Analyze large and diverse datasets to identify trends and insights.
• Develop, train, and validate predictive and classification models.
• Translate analytical findings into clear, actionable recommendations and present to stakeholders.

Skills

Python

R

Statistics

ML

Data Mining

Feature Engineering

Data Visualization

Stakeholder Communication

Tools

Tableau

Power BI

Matplotlib

Seaborn

Spark

Hadoop

AWS SageMaker

Azure ML

GCP AI Platform

Job description

Job Description

Job Description: Data Engineer / Data ScientistRole Overview

The Data Engineer / Data Scientist will be responsible for designing, developing, and maintaining data pipelines, performing data analysis, building predictive models, and ensuring data quality across platforms and operations.

Role Description

Responsible for extracting insights and value from complex datasets by applying statistical analysis, machine learning, and data visualization techniques. The role involves collaborating with business stakeholders to understand problems, developing predictive and prescriptive models, and communicating findings to support data-driven decision-making. Data Scientists work closely with data engineers and AI teams to operationalize models and ensure actionable outcomes.

Key Responsibilities

• Design, build, and optimize scalable data pipelines and ETL processes.
• Develop and maintain data models, data marts, and analytical datasets.
• Collaborate with cross-functional teams to gather requirements and deliver data-driven solutions.
• Perform exploratory data analysis (EDA) and create machine learning models as required.
• Implement data quality checks, validation rules, and monitoring processes.
• Automate data workflows and ensure timely availability of data.
• Work with cloud platforms such as Azure, AWS, or GCP for data engineering activities.

Technical Skills

• Proficient in programming languages such as Python or R
• Expertise in statistics, machine learning algorithms, and data mining techniques
• Experience with data preprocessing, feature engineering, and model validation
• Skilled in data visualization tools and libraries (Tableau, Power BI, Matplotlib, Seaborn)
• Familiarity with big data platforms (Spark, Hadoop) and SQL databases
• Knowledge of cloud-based ML platforms (AWS SageMaker, Azure ML, GCP AI Platform)
• Understanding of experimental design and A/B Testing
• Ability to communicate complex technical concepts to non-technical stakeholders

Key Responsibilities

• Analyze large and diverse datasets to identify trends, patterns, and insights
• Develop, train, and validate predictive and classification models
• Analyze and select the appropriate AI foundation models for use where appropriate
• Design experiments and conduct hypothesis testing to support business initiatives
• Collaborate with data engineers to prepare and optimize data for analysis
• Translate analytical findings into clear, actionable recommendations
• Present insights and reports to stakeholders using visual storytelling techniques
• Monitor and maintain models in production environments to ensure ongoing accuracy
• Stay updated on emerging tools, techniques, and best practices in data science