Data Scientist Needed for Predictive Analytics & Machine Learning Pipeline

We are looking for an accomplished Data Scientist to build robust predictive analytics solutions and end-to-end machine learning (ML) pipelines for business-critical applications. You will work closely with stakeholders to analyze large datasets, extract actionable insights, develop and deploy ML models, and drive data-driven decision-making across the organization.

Project Overview:

Design and implement scalable predictive models for various business use-cases using Python, TensorFlow/PyTorch, and cloud data tools. The ideal candidate will support the entire ML lifecycle: data ingestion, cleaning, feature engineering, model training, evaluation, deployment, and monitoring.

Key Responsibilities:

Collaborate with business and technical teams to define analytics requirements

Gather, clean, and preprocess structured and unstructured data

Conduct exploratory data analysis and statistical modeling

Engineer features and select optimal modeling strategies

Build, train, validate, and tune machine learning models (regression, classification, clustering, etc.)

Develop data pipelines for automated ETL processes

Deploy models using cloud-based solutions (AWS SageMaker, GCP AI Platform, Azure ML)

Implement model monitoring and retraining workflows

Visualize results with dashboards (Tableau, Power BI, Plotly, Dash)

Deliver presentations and documentation on findings and results

Required Skills:

3+ years in data science and ML projects

Expertise with Python & libraries (pandas, numpy, scikit-learn, TensorFlow/PyTorch)

Experience building production-ready ML solutions on cloud (AWS/GCP/Azure)

Strong data wrangling and feature engineering skills

Familiarity with SQL/NoSQL databases and big data tools (Spark, Hadoop)

Strong communication and presentation ability

Knowledge of version control systems (Git)

Comfort with model interpretability and validation

Technical Stack:

Languages: Python, R

ML Libraries: scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM

Big Data: Spark, Hadoop, Hive

Databases: PostgreSQL, MongoDB, BigQuery, Redshift

Dashboarding: Tableau, Power BI, Plotly, Dash

Cloud: AWS, GCP, Azure ML

Deployment: Docker, Kubernetes, Flask/FastAPI, Seldon Core

Data Science Tasks:

Predictive and prescriptive modeling

Text analytics and NLP

Time series forecasting

Segmentation and clustering

Recommendation systems

Anomaly detection

Dimensionality reduction/PCA

Automated data quality monitoring

Integration Requirements:

Integration with business dashboards

Secure REST API for model consumption

Data warehouse and data lake connectivity

Automated retraining and alerting

Performance Metrics:

Model accuracy (AUC, F1, RMSE, etc.)

Production deployment time

Prediction latency

Stakeholder adoption rate

Deliverables:

Production-ready ML models and codebase

ETL/data pipeline scripts

Model monitoring dashboards

Documentation and user training

Post-deployment support (2 weeks)

Use Cases:

Customer churn prediction

Sales and demand forecasting

Fraud detection

Marketing and personalization analytics

Supply chain optimization

Budget: $55-$105/hour (Hourly) or $12,000-$23,000 (Fixed project)

Timeline: 6-12 weeks