Job Role: Full-Stack Data Scientist (10+ Year)
Job Location: Pleasanton, CA Onsite (5days/week)
Job Type: Contract
Pay Range: $65 ā$70 /hour
Role Descriptions:
Must Have Technical/Functional Skills
Hands-on experience on:
1. Programming Languages
Strong Python familiarity (hands-on) for data prep, modeling, and building ML components.
SQL – Skills: joins, window functions, CTEs, query optimization
2. Machine Learning
Linear/Logistic Regression
Decision Trees, Random Forest, XGBoost, LightGBM
SVM, KNN
Model evaluation – Precision/Recall, F1, ROC-AUC, MSE, RMSE
Model tuning – Grid search, randomized search, cross-validation
3. Deep Learning
Frameworks: TensorFlow, Keras, PyTorch
CNNs, RNNs, LSTMs, Transformers
Use cases: NLP, computer vision, time-series forecasting
4. Data Wrangling & Preprocessing
Missing data handling
Feature engineering
Data cleaning
Outlier detection
Normalization/standardization
5. Data Visualization & BI Tools
Python: Matplotlib, Seaborn, Plotly
Tools: Tableau, Power BI Dashboards, reporting, storytelling with data
6. Big Data & Cloud Tools (Needed for production-scale roles)
Big Data Frameworks: Spark, Hadoop Cloud Platforms (any one strongly):
AWS (S3, EC2, SageMaker)
Azure (Data Factory, Databricks, ML Studio)
GCP (BigQuery, Vertex AI)
7. Deployment Skills (advanced roles)
Model deployment: Flask, FastAPI
Docker, Kubernetes (optional)
CI/CD basics
8. Databases & Data Engineering Basics
Relational: MySQL, PostgreSQL, SQL Server
NoSQL: MongoDB, Cassandra
Data pipelines: Airflow, Prefect (optional)
Roles & Responsibilities
Define the ML use case, success metrics, and evaluation criteria; Liaise with business directly and translate business needs intan ML approach.
Perform data exploration, data quality checks, feature engineering, and dataset preparation for training and testing.
Build, train, validate, and iterate ML models; compare experiments and select the best candidate model.
Package the solution for production (e.g., containerized scoring/service endpoint) and support deployment with engineering/MLOps practices
Set up basic monitoring (model accuracy/health) and support continuous improvement post-release. Required Skills & Experience
Solid foundation in ML concepts (supervised/unsupervised, evaluation, validation) and practical experimentation.
Experience taking models tproduction in a cloud-agnostic way (portable design; API/service mindset).
Working knowledge of version control and basic CI/CD-style collaboration with engineering teams.
Contact Information
Email: pankaj.singh@diverselynx.com
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