Here's the complete fixed implementation. The race condition is resolved by introducing a `threading.Lock` that synchronizes access to the shared `self.jobs` and `self.results` dictionaries. The lock is held only during dictionary operations to avoid blocking threads while the `processor` function runs.

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Lock to synchronize access to shared state
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # 1. Safely check existence and remove job from queue
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs.pop(job_id)
            
        # 2. Process outside the lock to allow concurrency
        result = processor(data)
        
        # 3. Safely store the result
        with self.lock:
            self.results[job_id] = result
            
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:
1. **Added `self.lock = threading.Lock()`** in `__init__` to protect shared dictionaries.
2. **Wrapped dictionary reads/writes in `with self.lock:`** blocks to prevent TOCTOU (Time-Of-Check-Time-Of-Use) races and concurrent modification issues.
3. **Used `self.jobs.pop(job_id)`** inside the lock to atomically retrieve and remove the job, preventing duplicate processing.
4. **Released the lock during `processor(data)`** so that CPU/IO-bound processing doesn't unnecessarily block other threads from adding or retrieving jobs.
5. **Protected `get_result`** with the lock to ensure consistent reads of `self.results`.