Data Scientist

Role: Data Scientist

Location: Atlanta, GA (Onsite) 

Duration: 12+ months

What We Expect from the Data Scientist

We are looking for a Data Scientist with strong analytical depth, statistical expertise, and hands-on experience working across the full data lifecycle—from raw data exploration to model evaluation and deployment.

1. Exploratory Data Analysis (EDA)

Perform in-depth exploratory data analysis to understand structure, distributions, correlations, and anomalies

Identify data quality issues (missing values, outliers, inconsistencies)

Generate meaningful insights using summary statistics and visualizations

Translate exploratory findings into actionable business hypotheses

Document assumptions and analytical findings clearly

2. Understanding of Data Sources

Strong understanding of structured and unstructured data sources

Experience working with relational databases (SQL), data warehouses, APIs, and third-party data providers

Ability to assess data reliability, completeness, and bias

Knowledge of data governance, data lineage, and metadata management

Collaborate with data engineering teams to ensure data availability and integrity

3. Data Pipelines & Data Engineering Awareness

Design and build scalable data pipelines for ingestion, transformation, and feature engineering

Experience with ETL/ELT processes

Work with batch and/or real-time data processing systems

Familiarity with big data and distributed processing frameworks

Ensure reproducibility and automation of data workflows

4. Statistical Expertise

Strong foundation in probability and statistical inference

Hypothesis testing, confidence intervals, p-values

Regression analysis (linear, logistic)

Time series analysis (if applicable)

Sampling techniques and bias mitigation

Understanding of statistical assumptions and their impact on models

Ability to compute and interpret statistical derivatives (rate of change, marginal effects, partial derivatives in modeling contexts)

5. Model Development & Evaluation

Develop predictive and classification models using appropriate algorithms

Apply cross-validation and proper train-test splitting

Evaluate models using relevant metrics (Accuracy, Precision, Recall, F1-score, ROC-AUC, RMSE, MAE, etc.)

Perform feature selection and feature importance analysis

Detect and mitigate overfitting and underfitting

Conduct model performance monitoring and re-training strategies

6. Statistical & Technical Tools (Expected Proficiency)

Programming: Python and/or R

Libraries: Pandas, NumPy, Scikit-learn, Statsmodels

Visualization: Matplotlib, Seaborn, Tableau or Power BI

Databases: SQL

Big Data Tools: Spark (preferred)

Version control: Git

I look forward to hearing from you.
Best regards, 

Ankit Kalia

Technical Recruiter - Metasys Technologies
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