Data Scientist

Company Description

Aventis Technology operates at the intersection of management consulting and custom IT services, assisting clients in achieving strategic business outcomes through technology. By integrating business expertise with deep technical capability, Aventis Technology transforms challenges into opportunities that drive growth, efficiency, and competitive advantage. The company partners with forward-thinking organizations to deliver innovative solutions tailored to their unique needs.

Role Description

This role can be part-time or full-time, and is offered as a remote position. We are looking for a Data Scientist with 5–7 years of experience in applied machine learning, statistical modeling, and data analytics.

Key Responsibilities

Build predictive and statistical models (classification, regression, uplift, clustering, forecasting).

Develop feature-engineering pipelines using large datasets from various sources.

Conduct exploratory data analysis (EDA) to identify patterns, segment behaviors, and business opportunities.

Collaborate with product, data engineering, and business teams to define analytical requirements.

Design and evaluate A / B / n experiments and support experimentation frameworks.

Create production-ready model pipelines in collaboration with ML engineers.

Monitor model performance, drift, and KPI impact over time.

Present insights and recommendations clearly to technical and non-technical stakeholders.

Required Skills

5–7 years of experience in Data Science, Machine Learning, or Applied Analytics.

Strong programming experience in Python (Pandas, NumPy, Scikit-learn) and SQL.

Proficiency in ML algorithms : tree-based models, logistic regression, clustering (k-means), time-series, uplift modeling, etc.

Solid understanding of statistics, probability, hypothesis testing, and experiment design.

Hands-on experience with cloud platforms (AWS, Azure, GCP) or Databricks.

Familiarity with model deployment workflows and MLOps fundamentals.

Nice to Have

Experience with telecom analytics (churn, segmentation, ARPU, price elasticity).

Exposure to recommender systems, reinforcement learning, or simulation models.