Senior Data Scientist

Senior Data Scientist

Experience Required: 6-8 years in data science or related field

About the Role

We are seeking an experienced Senior Data Scientist to join our team and help our clients drive data-driven decision making across our organization. In this role, you will leverage your deep expertise in statistical analysis, machine learning, and predictive modeling to solve complex business problems and deliver actionable insights using cutting-edge technologies.

Key Responsibilities

• Lead end-to-end data science projects from problem definition through deployment and monitoring
• Conduct comprehensive exploratory data analysis (EDA) to uncover patterns, anomalies, and opportunities in complex datasets
• Design and implement advanced statistical models and machine learning algorithms to solve business challenges
• Develop and deploy predictive models and time series forecasting solutions for various business applications
• Build and optimize large language model (LLM) applications and fine-tune foundation models
• Collaborate with cross-functional teams to translate business requirements into analytical solutions
• Mentor junior data scientists and contribute to team knowledge sharing
• Present findings and recommendations to technical and non-technical stakeholders
• Ensure model performance through continuous monitoring and optimization

Required Qualifications

• 6-8 years of hands-on experience in data science, machine learning, or related field
• Master's degree in Computer Science, Statistics, Mathematics, or related quantitative field
• Strong Statistical Foundation: Deep understanding of statistical concepts including hypothesis testing, probability distributions, regression analysis, and experimental design
• EDA Expertise: Proven ability to explore and analyze complex datasets using advanced visualization and statistical techniques
• Machine Learning Proficiency:

Extensive experience with supervised and unsupervised learning algorithms

Strong understanding of algorithm selection, feature engineering, and model evaluation

Hands-on experience with modern deep learning frameworks (PyTorch, JAX, TensorFlow 2.x)

Experience with transformer architectures and attention mechanisms

• Predictive Analytics: Demonstrated success in building and deploying predictive models in production environments
• Time Series Analysis: Expertise in time series forecasting methods like ARIMA, Prophet, BSTS, and modern deep learning approaches (LSTM, Temporal Fusion Transformers)
• Technical Skills:

Proficiency in Python with modern libraries (Polars, DuckDB, PyArrow)

Experience with ML frameworks (XGBoost, LightGBM, CatBoost, scikit-learn)

Strong SQL skills and experience with modern data warehouses (Snowflake, BigQuery, Databricks)

Experience with cloud platforms and associated AI/ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML, etc)

Proficiency with version control (Git) and modern development practices

Preferred Qualifications

• MLOps & Modern ML Infrastructure:

Experience with MLflow, Weights & Biases, or Neptune for experiment tracking

Familiarity with feature stores (Feast, Tecton, AWS Feature Store)

Experience with model serving frameworks (BentoML, Seldon Core, KServe)

Knowledge of vector databases (Pinecone, Weaviate, Qdrant) for embedding-based applications

Understanding of model monitoring tools (Evidently AI, Fiddler, Arize)

• LLM & Generative AI Experience:

Hands-on experience with LLM APIs (OpenAI, Anthropic etc)

Familiarity with prompt engineering and LLM application frameworks (LangChain, LlamaIndex, Langflow)

Experience with open-source LLMs (Llama, Mistral, Gemma) and fine-tuning techniques

Knowledge of RAG (Retrieval Augmented Generation) architectures

• Modern Data Stack:

Experience with dbt for data transformation

Familiarity with orchestration tools (Dagster, Prefect, Apache Airflow)

Knowledge of data quality tools (Great Expectations, Soda)

Experience with streaming data platforms (Kafka, Pulsar, Kinesis)

• Advanced Analytics:

Experience with AutoML platforms , AutoGluon, FLAML)

Knowledge of causal inference methods and libraries (DoWhy, CausalML)

Familiarity with privacy-preserving ML techniques (differential privacy, federated learning)

• Published research or contributions to open-source projects
• Experience leading data science initiatives and managing stakeholder expectations

What We Offer

• Opportunity to work on challenging problems with real business impact
• Access to cutting-edge tools, technologies, and GPU resources
• Continuous learning opportunities including conference attendance and training budgets
• Collaborative environment with talented data professionals
• Modern development infrastructure