Data Scientist

Data Radian Technologies; United States; Remote; USD 80,000 - 120,000; Full time

Job summary

An established industry player is seeking a skilled data scientist with expertise in causal inference and experimentation. This role involves designing and analyzing experiments to uncover causal relationships, developing forecasting models to predict key business metrics, and collaborating with cross-functional teams to drive data-driven strategies. The ideal candidate will have a strong foundation in Python and R, proficiency in SQL, and experience with advanced analytical tools. Join a dynamic environment where your analytical skills will directly influence business outcomes and strategic decisions.

Qualifications

• 5+ years of experience in data science focusing on causal inference and experimental design.
• Proficiency in SQL and strong skills in Python and R for data analysis.

Responsibilities

• Design and analyze experiments to identify causal relationships between variables.
• Develop time-series forecasting models to predict business metrics.

Skills

Causal Inference

Forecasting

Python

R

Data Wrangling

SQL

Tools

DoWhy

CausalML

EconML

Tableau

Power BI

Matplotlib

Seaborn

Job description

Key Responsibilities

1. Causal Inference & Experimentation
   1. Design and analyze experiments (A/B tests, observational studies) to identify causal relationships between variables.
   2. Apply advanced causal inference methods (e.g., propensity score matching, difference-in-differences, instrumental variables, DAGs) to estimate treatment effects.
   3. Use tools like DoWhy, CausalML, or EconML to model causal relationships.
2. Forecasting & Multivariate Analysis
   1. Develop time-series forecasting models (e.g., ARIMA, Prophet, LSTM) to predict business metrics (sales, demand, churn).
   2. Perform multivariate regression analysis to identify key drivers of outcomes.
   3. Validate model accuracy and refine approaches based on feedback.
3. Data Analysis & Visualization
   1. Clean, preprocess, and analyze large datasets to extract meaningful patterns.
   2. Build dashboards and reports to communicate findings to stakeholders using tools like Tableau, Power BI, or Python (Matplotlib/Seaborn).
4. Collaboration
   1. Partner with product, marketing, and operations teams to define KPIs and design data-driven strategies.
   2. Translate complex technical results into actionable business recommendations.

Qualifications

1. Technical Skills
   1. Causal Inference: Proficiency in methods like propensity score matching, synthetic controls, and counterfactual analysis.
   2. Forecasting: Experience with time-series models (Prophet, ARIMA) and machine learning frameworks (Scikit-learn, TensorFlow).
   3. Programming: Strong Python/R skills (Pandas, NumPy, StatsModels, CausalInference).
   4. Data Wrangling: SQL proficiency for querying large datasets.
2. Experience
   1. 5+ years in data science, with a focus on causal inference and experimental design.
   2. Experience in industries like e-commerce, retail, or tech is a plus.