Senior Account Manager - Data Science

3 Months ago • All levels

Data Analysis

Job Description

This Senior Account Manager position requires a data scientist with strong technical skills in machine learning and statistical analysis. The role involves designing and implementing predictive models, collaborating with stakeholders to address business challenges, and driving strategic decisions through data-driven insights. Key responsibilities include model development, data-driven business strategy, and analytics & experimentation. This encompasses tasks from designing machine learning solutions to performing exploratory analysis and automating data pipelines. The ideal candidate will translate business problems into data science projects and provide actionable recommendations.

Good To Have:

Experience in Gitlab and CI/CD pipelines.
Familiarity with AWS, GCP, or Azure services.

Must Have:

Design and implement machine learning solutions (regression, classification, NLP).
Evaluate and tune models using appropriate metrics.
Work on feature engineering and model interpretability.
Proficient in Python and MySQL.
Proficient in statistical techniques and hypothesis testing.

Add these skills to join the top 1% applicants for this job

business-strategy

data-analytics

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gitlab

postgresql

mysql

aws

azure

seaborn

plotly

matplotlib

data-science

numpy

scikit-learn

pytorch

pandas

ci-cd

python

tensorflow

machine-learning

We are seeking a data scientist with strong technical expertise in machine learning and statistical analysis to develop and deploy impactful data solutions. The role involves designing and implementing predictive models (e.g., regression, classification, NLP, time series), collaborating with stakeholders to solve business problems, and driving strategic decisions through experimentation and data-driven insights

Job Description:

Key responsibilities:

1. Modeling & Machine Learning

Design and implement robust machine learning solutions (e.g., regression, classification, NLP, time series, recommendation systems).
Evaluate and tune models using appropriate metrics (AUC, RMSE, precision/recall, etc.).
Work on feature engineering, model interpretability, and performance optimization.

2. Data-Driven Business Strategy

Partner with stakeholders to identify key opportunities where data science can drive business value.
Translate business problems into data science projects with clearly defined deliverables and success metrics.
Provide actionable recommendations based on data analysis and model outputs.

3. Analytics and Experimentation

Conduct deep-dive exploratory analysis to uncover trends and insights.
Apply statistical methods to test hypotheses, forecast trends, and measure campaign effectiveness
Design and analyze A/B tests and other experiments to support product and marketing decisions.
Automate data pipelines and dashboards for ongoing monitoring of model and business performance.

Technical Skills:

Languages: Proficient in Python, MySQL.
Libraries/Frameworks: scikit-learn, Pandas, NumPy, Time series, ARIMA, Bayesian model, Market Mix model, Regression, XGBoost, LightGBM, TensorFlow, PyTorch.
Statistical Method: Proficient in statistical techniques such as hypothesis testing, regression analysis, time-series analysis
Databases : PostgreSQL, BigQuery, MySQL.
Visualization: Plotly, Seaborn, Matplotlib.
Deployment: Experience in Gitlab, CI/CD pipelines(Good to have).
Cloud Platforms: Familiarity with AWS, GCP, or Azure services(Good to have).