Description :
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 fieldMaster's degree in Computer Science, Statistics, Mathematics, or related quantitative fieldStrong Statistical Foundation : Deep understanding of statistical concepts including hypothesis testing, probability distributions, regression analysis, and experimental designEDA Expertise : Proven ability to explore and analyze complex datasets using advanced visualization and statistical Machine Learning Proficiency :1. Extensive experience with supervised and unsupervised learning algorithms
2. Strong understanding of algorithm selection, feature engineering, and model evaluation
3. Hands-on experience with modern deep learning frameworks (PyTorch, JAX, TensorFlow 2.x)
4. Experience with transformer architectures and attention mechanisms
Predictive Analytics : Demonstrated success in building and deploying predictive models in production environmentsTime 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 practicesPreferred Qualifications :
1. MLOps & Modern ML Infrastructure :
Experience with MLflow, Weights & Biases, or Neptune for experiment trackingFamiliarity 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 applicationsUnderstanding of model monitoring tools (Evidently AI, Fiddler, Arize)2. 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 techniquesKnowledge of RAG (Retrieval Augmented Generation) architectures3. Modern Data Stack :
Experience with dbt for data transformationFamiliarity with orchestration tools (Dagster, Prefect, Apache Airflow)Knowledge of data quality tools (Great Expectations, Soda)Experience with streaming data platforms (Kafka, Pulsar, Kinesis)4. Advanced Analytics :
Experience with AutoML platforms (H2O.ai, 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 projectsExperience leading data science initiatives and managing stakeholder expectationsWhat We Offer :
Opportunity to work on challenging problems with real business impactAccess to cutting-edge tools, technologies, and GPU resourcesContinuous learning opportunities including conference attendance and training budgetsCollaborative environment with talented data professionalsModern development infrastructure(ref : hirist.tech)