Senior Data Scientist

Role - Senior Data Scientist
Location - Pleasanton, CA
Duration - Fulltime Role

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):
o WS (S3, EC2, SageMaker)
o zure (Data Factory, Databricks, ML Studio)
o 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 into an 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 f or 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 to production in a cloud agnostic way (portable design; API/service mindset).
• Working knowledge of version control and basic CI/CD-style collaboration with engineering teams.


Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.