Data Scientist

Acestack; Pittsburgh (Allegheny County); On-site; USD 90,000 - 130,000; Full time

Job summary

Acestack in Pittsburgh, PA is seeking a Data Scientist to develop and deploy ML models, perform data wrangling, and deliver actionable insights for business decisions. The role emphasizes hands-on modeling, statistical analysis, and building robust data pipelines in an onsite setting.

You will collaborate with product, engineering, and leadership teams to translate complex analyses into practical dashboards and recommendations, shaping strategies with data-driven evidence.

Qualifications

• Proficiency in Python or R and SQL for data querying.
• Experience with ML, statistical modeling, data mining, and big data frameworks.
• Experience with data visualization tools like Tableau, Power BI, or Excel.

Responsibilities

• Prepare, clean, and transform large datasets (often 70–90% of work).
• Perform Exploratory Data Analysis to identify trends and correlations.
• Develop, test, and validate machine learning and statistical models.
• Translate findings into clear charts, dashboards, and business recommendations for non-technical audiences.
• Collaborate with data engineers, product teams, and management to deploy models into production.

Skills

Python

R

SQL

Machine learning

Statistical modeling

Data mining

Tools

Hadoop

Spark

Tableau

Power BI

Excel

Job description

Job Title: Data Scientist

Location: Pittsburgh, PA (Onsite) Fulltime

Job Description

Skill: Data Scientist

Must Have Technical/Functional Skills:

• Programming Languages: Proficiency in Python or R, plus SQL for database querying.
• Technical Skills: Machine learning, statistical modeling, data mining, and big data frameworks (e.g., Hadoop, Spark).
• Data Visualization & Tools: Experience with tools like Tableau, Power BI, or Excel.
• Soft Skills: Strong analytical thinking, problem-solving, and the ability to tell a compelling story with data.

Roles & Responsibilities:

• Data Preparation & Cleaning: Spend a large portion of time (often 70% to 90%) gathering, cleaning, and transforming raw structured and unstructured data.
• Exploratory Data Analysis (EDA): Identify trends, patterns, and correlations using statistical methods and visualization tools.
• Model & Algorithm Development: Build, test, and validate machine learning models, statistical models, and predictive algorithms.
• Stakeholder Communication: Translate technical findings into clear charts, dashboards, and business recommendations for non-technical teams.
• Cross-Functional Collaboration: Partner with data engineers, product teams, and management to integrate models into production.