Data & AI Junior Consultant

PwC•Belgrade, MT

78d

About The Position

We're looking for an early-career Data Scientist / ML Engineer to help us design and ship practical, data-driven ML and GenAI prototypes. If you have strong fundamentals, hands-on projects or internships, and a passion for building with LLMs, this role is a great way to accelerate your growth while contributing to real product impact. We offer: Strong opportunities for professional and career growth A stable work environment that supports long-term planning Competitive compensation and benefits The chance to work with high-profile clients across Europe Excellent career development opportunities Mentorship, training, and clear development paths Modern tooling and a cloud-first stack

Requirements

0 - 2 years of relevant experience (internships and projects count)
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Math, Engineering, or equivalent experience
Willingness to learn fast, build usable ML/GenAI solutions, and grow with a supportive team
Python: pandas, numpy, scikit-learn; basic scripting and unit testing
SQL: joins, window functions, performance basics
ML/DS: supervised learning, cross-validation, model metrics, handling missing/imbalanced data
Visualization: Matplotlib/Seaborn/Plotly; clear storytelling with charts
GenAI: experience with LLM APIs (e.g., OpenAI/Anthropic), prompts, embeddings; build simple RAG workflows
Agentic AI: experience with agent frameworks and patterns (e.g., LangChain Agents/LangGraph, OpenAI Assistants, CrewAI, AutoGen); tool/function calling, planning loops, memory, multi-agent coordination; guardrails and evaluation best practices
Tools: Git, notebooks, MLflow or Weights & Biases (basic usage); familiarity with LangChain/LangGraph or similar; tracing/observability for agent pipelines (e.g., LangSmith, OpenTelemetry)
Cloud basics: familiarity with AWS/GCP/Azure services; using Docker locally
Statistics fundamentals: hypothesis testing, confidence intervals, experiment design basics
Communication: clear writing, documenting, and presenting results

Nice To Haves

PyTorch or TensorFlow; LoRA/PEFT familiarity
Vector databases (FAISS, Pinecone, Weaviate); Elasticsearch/OpenSearch basics
Data warehousing (Snowflake/BigQuery/Redshift); dbt basics
Orchestration (Airflow/Dagster) and simple CI/CD
BI tools (Looker/Tableau/Power BI)
Portfolio: GitHub, Kaggle, blogs, or open-source contributions
Previous consulting experience

Responsibilities

Clean, analyze, and prepare datasets; perform EDA and build baseline models
Implement feature engineering; run model evaluation and simple A/B tests
Develop GenAI and Agentic AI prototypes using LLM APIs and agent frameworks (prompting, embeddings, tool/function calling, basic RAG)
Design and implement agent workflows: planning/execution loops, memory, tool integrations (APIs, databases), and multi-step/multi-agent orchestration
Evaluate agent performance and reliability; define success metrics, add guardrails/fallbacks, and optimize for cost/latency
Collaborate with engineers and analysts on data pipelines and dashboards